Take all the input files, attach their unique_id, find the ones with replicates, then look at the median of 2*SD (~95% spread around measurement mean) for all measurements to decided on an approriate significance threshold
file_vector = str_remove(list.files(path = "Input", pattern="*.csv", recursive = T), ".csv")
error_set = lapply(paste0("Input/", list.files(path = "Input", pattern="*.csv", recursive = T)), read_csv, skip = 5, col_names = F, col_select = 1:2, show_col_types=F)
for(i in 1:length(error_set)){
error_set[[i]][,3] = file_vector[i]
}
error_set = do.call("rbind", error_set)
colnames(error_set) = c("Genotype", "Phenotype", "unique_id")
error_rates = error_set[which(duplicated(error_set %>%
unite(unique_geno, c(1,3), sep = "_", remove = F) %>%
pull(unique_geno))),] %>%
group_by(Genotype) %>%
summarise(pheno_mean = mean(Phenotype),
pheno_sd = sd(Phenotype))
## % of SDs for measurements that are less than 1.5-fold threshold
sum(error_rates$pheno_sd*2 <= log10(1.5)) / length(error_rates$pheno_sd)
## [1] 0.5170068
median(error_rates$pheno_sd*2)
## [1] 0.1705538
summary(error_rates$pheno_sd*2)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.08511 0.17055 0.71405 0.74638 3.73452
error_rates %>%
ggplot(aes(x = pheno_sd*2)) +
geom_histogram(bins = 50) +
geom_vline(xintercept = log10(1.5), lty = 2) +
theme_classic() +
theme(text = element_text(size=18), axis.text = element_text(size = 16, color = "black"),
axis.title.y = element_blank(),
axis.title.x = element_blank(),
legend.position = "none")
## Fold-Change Data
Here we look at the fold-change data used in the input files. This allows us to see positive and negative functional effects across the landscapes.
fit_land = do.call("rbind", lapply(paste0("Output/", list.files(path = "Output", pattern="observed_values.csv", recursive = T)), read_csv, skip = 1, show_col_types=F, col_names = c("id", "effect", "muts", "junk1", "junk2", "enz")))
## Make fit_land have IDs by finding where muts are 0, i.e. start of new dataset, and multiplying
## Those values by the folder names to generate ID vector
fit_land_spots = c(which(fit_land$muts == 0), (dim(fit_land)[1] + 1)) # Add end of dataset
fit_land_counts = c()
for(i in 1:(length(fit_land_spots) - 1)) {
if(i == 1) {
fit_land_counts = c(fit_land_counts, fit_land_spots[i + 1] - 1)
} else {
fit_land_counts = c(fit_land_counts, fit_land_spots[i + 1] - fit_land_spots[i])
}
}
## This assumes folders are read in the order that they are listed
fit_land_ids = rep(str_remove_all(list.files(path = "Output", pattern="observed_values.csv", recursive = T), "/observed_values.csv"), fit_land_counts)
fit_land$unique_id = fit_land_ids
fit_land = fit_land %>% filter(muts != 0)
The data shows the following distribution
c(sum(fit_land$effect < log10(1/1.5)), sum(fit_land$effect >= log10(1/1.5) & fit_land$effect <= log10(1.5)), sum(fit_land$effect > log10(1.5)))
## [1] 360 292 807
And these percentages
c(sum(fit_land$effect < log10(1/1.5)), sum(fit_land$effect >= log10(1/1.5) & fit_land$effect <= log10(1.5)), sum(fit_land$effect > log10(1.5))) / sum(c(sum(fit_land$effect < log10(1/1.5)), sum(fit_land$effect >= log10(1/1.5) & fit_land$effect <= log10(1.5)), sum(fit_land$effect > log10(1.5))))
## [1] 0.2467443 0.2001371 0.5531186
fit_land_plot = fit_land %>%
ggplot(aes(x = effect)) +
geom_histogram(bins = 100, alpha = 0.8, color = "black", lwd = 0.1) +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(1/1.5), lty = 2, lwd = 0.2) +
theme_classic() +
labs(x = expression(italic("F")),
y = "Genotype Counts") +
scale_y_continuous(breaks = c(0, 50, 100, 150, 200, 250)) +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"))
fit_land_plot
#ggsave("supp_fig_1.svg", plot = fit_land_plot, width = 180/2, height = 247/4, dpi = 300, units = "mm")
First we import the ratio datasets, which contains ratio values between predicted function based on the additive model vs observed function. These ratio values serve as a general metric for “epistasis” as defined by the additive model.
all_ratios = lapply(paste0("Output/", list.files(path = "Output", pattern="ratio_export.csv", recursive = T)), read_csv, skip = 1, show_col_types=F, col_names = c("id", "effect", "muts", "cv", "colors"))
all_ratios_id = str_remove_all(list.files(path = "Output", pattern="ratio_export.csv", recursive = T), "/ratio_export.csv")
temp_col_length = length(all_ratios[[1]]) + 1
for(i in 1:length(all_ratios)) {
all_ratios[[i]][, temp_col_length] = rep(all_ratios_id[i], dim(all_ratios[[i]])[1]) ## Add partial_id as a column to each ratio df in list
}
rm(temp_col_length)
## Make into a tibble
all_ratios = do.call("rbind", all_ratios)
colnames(all_ratios) = c("id", "effect", "muts", "cv", "colors", "partial_id")
Filtering ratio data to only include genotypes with 2+ mutations. Outputting length of vector.
all_ratios = all_ratios %>% filter(muts > 1)
length(all_ratios$effect) # examined muts
## [1] 1245
Number and % of synergystic genotypes (i.e. ratio > 1.5-fold)
sum(all_ratios$effect > 1.5) # synergystic
## [1] 502
paste0("Synergystic ", round(sum(all_ratios$effect > 1.5)/length(all_ratios$effect)*100,2), "%", collapse = "") # synergystic %
## [1] "Synergystic 40.32%"
Number and % of neutral genotypes (i.e. ratio > 1.5-fold)
sum(all_ratios$effect >= 1/1.5 & all_ratios$effect <= 1.5) # antagonistic
## [1] 379
paste0("Neutral ", round(sum(all_ratios$effect >= 1/1.5 & all_ratios$effect <= 1.5)/length(all_ratios$effect)*100, 2), "%", collapse = "") # antagonistic %
## [1] "Neutral 30.44%"
Number and % of antagonistic genotypes (i.e. ratio > 1.5-fold)
sum(all_ratios$effect < 1/1.5) # antagonistic
## [1] 364
paste0("Antagonistic ", round(sum(all_ratios$effect < 1/1.5)/length(all_ratios$effect)*100, 2), "%", collapse = "") # antagonistic %
## [1] "Antagonistic 29.24%"
Shows distribution of positive and negative epistasis across the landscapes. The significance threshold is 1.5-fold
all_ratios_plot = all_ratios %>%
ggplot(aes(x = log10(effect))) +
geom_histogram(bins = 100, alpha = 0.8, color = "black", lwd = 0.1) +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(1/1.5), lty = 2, lwd = 0.2) +
theme_classic() +
scale_fill_manual(values = c("grey")) +
labs(x = expression("Observed" *italic(" F")*" / Predicted"*italic(" F")),
y = "Genotype Counts") +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none")
all_ratios_plot
#ggsave("supp_fig_2.svg", plot = all_ratios_plot, width = 180/2, height = 247/4, dpi = 300, units = "mm")
Also the ratios table. Here we create bins from -Inf to log10(1/1.5), i.e. -0.176, then the neutral range between log10(1/1.5) to log10(1.5), i.e. 0.176, then the positive range from 0.176 to Inf.
all_ratios %>%
mutate(sign = cut(log10(effect), breaks = c(-Inf, log10(1/1.5), log10(1.5), Inf))) %>%
count(sign) %>%
mutate(percent = round(n/sum(n) * 100, 2))
## # A tibble: 3 × 3
## sign n percent
## <fct> <int> <dbl>
## 1 (-Inf,-0.176] 364 29.2
## 2 (-0.176,0.176] 379 30.4
## 3 (0.176, Inf] 502 40.3
We can do the same thing for individual orders: 2nd, 3rd, and 4th order
## 2nd step only
all_ratios_2 = all_ratios %>% filter(muts == 2)
length(all_ratios_2$effect) # examined muts
## [1] 419
sum(all_ratios_2$effect > 1.5) # synergystic
## [1] 135
paste0("Synergystic ", round(sum(all_ratios_2$effect > 1.5)/length(all_ratios_2$effect)*100, 2), "%", collapse = "") # synergystic %
## [1] "Synergystic 32.22%"
sum(all_ratios_2$effect >= 1/1.5 & all_ratios_2$effect <= 1.5) # antagonistic
## [1] 160
paste0("Neutral ", round(sum(all_ratios_2$effect >= 1/1.5 & all_ratios_2$effect <= 1.5)/length(all_ratios_2$effect)*100, 2), "%", collapse = "") # antagonistic %
## [1] "Neutral 38.19%"
sum(all_ratios_2$effect < 1/1.5) # antagonistic
## [1] 124
paste0("Antagonistic ", round(sum(all_ratios_2$effect < 1/1.5)/length(all_ratios_2$effect)*100, 2), "%", collapse = "") # antagonistic %
## [1] "Antagonistic 29.59%"
## 3rd step only
all_ratios_3 = all_ratios %>% filter(muts == 3)
length(all_ratios_3$effect) # examined muts
## [1] 438
sum(all_ratios_3$effect > 1.5) # synergystic
## [1] 160
paste0("Synergystic ", round(sum(all_ratios_3$effect > 1.5)/length(all_ratios_3$effect)*100, 2), "%", collapse = "") # synergystic %
## [1] "Synergystic 36.53%"
sum(all_ratios_3$effect >= 1/1.5 & all_ratios_3$effect <= 1.5) # antagonistic
## [1] 120
paste0("Neutral ", round(sum(all_ratios_3$effect >= 1/1.5 & all_ratios_3$effect <= 1.5)/length(all_ratios_3$effect)*100, 2), "%", collapse = "") # antagonistic %
## [1] "Neutral 27.4%"
sum(all_ratios_3$effect < 1/1.5) # antagonistic
## [1] 158
paste0("Antagonistic ", round(sum(all_ratios_3$effect < 1/1.5)/length(all_ratios_3$effect)*100, 2), "%", collapse = "") # antagonistic %
## [1] "Antagonistic 36.07%"
## 4th step only
all_ratios_4 = all_ratios %>% filter(muts == 4)
length(all_ratios_4$effect) # examined muts
## [1] 267
sum(all_ratios_4$effect > 1.5) # synergystic
## [1] 127
paste0("Synergystic ", round(sum(all_ratios_4$effect > 1.5)/length(all_ratios_4$effect)*100, 2), "%", collapse = "") # synergystic %
## [1] "Synergystic 47.57%"
sum(all_ratios_4$effect >= 1/1.5 & all_ratios_4$effect <= 1.5) # antagonistic
## [1] 72
paste0("Neutral ", round(sum(all_ratios_4$effect >= 1/1.5 & all_ratios_4$effect <= 1.5)/length(all_ratios_4$effect)*100, 2), "%", collapse = "") # antagonistic %
## [1] "Neutral 26.97%"
sum(all_ratios_4$effect < 1/1.5) # antagonistic
## [1] 68
paste0("Antagonistic ", round(sum(all_ratios_4$effect < 1/1.5)/length(all_ratios_4$effect)*100, 2), "%", collapse = "") # antagonistic %
## [1] "Antagonistic 25.47%"
Here we import the functional contribution of single positions (not combinations), which resembles first order analysis across all datasets.
## Reads all csvs in collated folder and combines them
d = do.call("rbind", lapply(paste0("Output/", list.files(path = "Output", pattern="*2d_box.csv", recursive = T)), read_csv, show_col_types=F, col_names = c("positions", "identity", "mut", "effects", "enzyme", "type", "cond")))
d1 = d %>% filter_all(any_vars(!is.na(.))) # removes rows with all NA before making unique ID
d1 = d1 %>%
unite("unique_id", c(positions, enzyme, type, cond), remove = F)
d1 = d1 %>%
unite("partial_id", c(enzyme, type, cond), remove = F)
Spread of functional contributions of the positions. This shows whether introducing a mutation in a given position impacts the function positively or negatively (or neutral) across all backgrounds
d1_plot = d1 %>%
ggplot(aes(x = effects)) +
geom_histogram(bins = 100, alpha = 0.8, color = "black", lwd = 0.1, fill = "#edae49") +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(1/1.5), lty = 2, lwd = 0.2) +
theme_classic() +
labs(x = expression(Delta*italic("F")),
y = "Genotype Count") +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none")
d1_plot
#ggsave("fig_1A.svg", plot = d1_plot, width = 180/2, height = 247/4, dpi = 300, units = "mm")
Density plots for each trajectory by enzymes
d1_plot_enz = d1 %>%
ggplot(aes(x = effects, fill = enzyme)) +
geom_density(alpha = 0.8, color = "black", lwd = 0.1) +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(1/1.5), lty = 2, lwd = 0.2) +
theme_classic() +
labs(x = expression(Delta*italic("F")),
y = "Genotype Count") +
#scale_y_continuous(breaks = c(0, 50, 100, 150, 200, 250)) +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"))
d1_plot_enz
### For WT-normalized data
Load the same kind of data as d1 except that has been normalized do the \(\Delta\)F in the WT background
wt_rel_df = do.call("rbind", lapply(paste0("Output/", list.files(path = "Output", pattern="*wt_rel.csv", recursive = T)), read_csv, show_col_types=F, col_names = c("positions", "identity", "mut", "effects", "enzyme", "type", "cond")))
wt_rel_df = wt_rel_df %>% filter_all(any_vars(!is.na(.))) # removes rows with all NA before making unique ID
wt_rel_df = wt_rel_df %>%
unite("unique_id", c(positions, enzyme, type, cond), remove = F) %>%
unite("partial_id", c(enzyme, type, cond), remove = F)
Then we plot it
wt_rel_plot = wt_rel_df %>%
filter(mut > 1) %>% # filter out first mutations which are by definition 0 delta F
ggplot(aes(x = effects)) +
geom_histogram(bins = 100, alpha = 0.8, color = "black", lwd = 0.1, fill = "#edae49") +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(1/1.5), lty = 2, lwd = 0.2) +
theme_classic() +
labs(x = expression(Delta*italic("F ")*"(Single Mutant Normalized)"),
y = "Genotype Count") +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none")
wt_rel_plot
#ggsave("fig_1A.svg", plot = d1_plot, width = 180/2, height = 247/4, dpi = 300, units = "mm")
And again for density plots for each enzyme
wt_rel_plot_enz = wt_rel_df %>%
mutate(Enzyme = enzyme) %>%
filter(mut > 1) %>%
ggplot(aes(x = effects, fill = Enzyme)) +
geom_density(alpha = 0.8, color = "black", lwd = 0.1) +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(1/1.5), lty = 2, lwd = 0.2) +
theme_classic() +
labs(x = expression(Delta*italic("F ")*"(Single Mutant Normalized)"),
y = "Density") +
scale_x_continuous(limits = c(-2.5, 2.5)) + # Same data omitted for better resolution
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"))
wt_rel_plot_enz
## Warning: Removed 77 rows containing non-finite values (stat_density).
Given this spread which percentage is significantly (using log10 1.5-fold cutoff) negative, neutral, and positive in that order
wt_rel_df %>%
filter(mut > 1) %>%
mutate(sign = cut(effects, breaks = c(-Inf, log10(1/1.5), log10(1.5), Inf))) %>%
count(sign) %>%
mutate(percent = round(n/sum(n) * 100, 2))
## # A tibble: 3 × 3
## sign n percent
## <fct> <int> <dbl>
## 1 (-Inf,-0.176] 942 24.5
## 2 (-0.176,0.176] 1438 37.4
## 3 (0.176, Inf] 1470 38.2
Looks at 2*SD of each position/combination across all orders to determine functional heterogeneity.
This gives us a quick look at the general spread of heterogenity values and their mean.
idio_d = do.call("rbind", lapply(paste0("Output/", list.files(path = "Output", pattern="idio_df", recursive = T)), read_csv, show_col_types=F))
traj_col = sapply(str_split(list.files(path = "Output", pattern="idio_df", recursive = T), "/"),"[[",1)
traj_col_len = sapply(lapply(paste0("Output/", list.files(path = "Output", pattern="idio_df", recursive = T)), read_csv, show_col_types=F), dim)[1,]
traj_col_full = c()
for(i in 1:length(traj_col)) {
traj_col_full = c(traj_col_full, rep(traj_col[i], traj_col_len[i]))
}
idio_d$traj = traj_col_full
idio_d_order_1 = idio_d %>%
filter(mutations == "Order 1") %>%
ggplot(aes(x = idiosync)) +
geom_histogram(binwidth = 0.08, color = "black", lwd = 0.1, fill = "#edae49") +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(2), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(10), lty = 2, lwd = 0.2) +
labs(x = expression("2"*sigma),
y = "Position Count") +
theme_classic() +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none")
idio_d_order_1
#ggsave("fig_1B.svg", idio_d_order_1, width = 180/2, height = 247/4, dpi = 300, units = "mm")
First order positions which have a heterogeneity index > log10 1.5-fold, 2-fold, 5-fold, and 10-fold for adaptive trajectories
idio_d %>%
filter(mutations == "Order 1") %>%
mutate(idiosync_1.5 = idiosync > log10(1.5),
idiosync_2 = idiosync > log10(2),
idiosync_5 = idiosync > log10(5),
idiosync_10 = idiosync > log10(10)) %>%
group_by(mutations) %>%
summarise(idiosync_sig_1.5 = sum(idiosync_1.5),
idiosync_sig_2 = sum(idiosync_2),
idiosync_sig_5 = sum(idiosync_5),
idiosync_sig_10 = sum(idiosync_10),
idiosync_total = length(idiosync),
idiosync_1.5 = sum(idiosync_1.5)/length(idiosync_1.5) * 100,
idiosync_2 = sum(idiosync_2)/length(idiosync_2) * 100,
idiosync_5 = sum(idiosync_5)/length(idiosync_5) * 100,
idiosync_10 = sum(idiosync_10)/length(idiosync_10) * 100
) %>%
knitr::kable()
| mutations | idiosync_sig_1.5 | idiosync_sig_2 | idiosync_sig_5 | idiosync_sig_10 | idiosync_total | idiosync_1.5 | idiosync_2 | idiosync_5 | idiosync_10 |
|---|---|---|---|---|---|---|---|---|---|
| Order 1 | 211 | 205 | 150 | 114 | 214 | 98.59813 | 95.79439 | 70.09346 | 53.27103 |
Histogram to show heterogenity at Orders 2, 3, and 4 with the log10 1.5-fold significance threshold
idio_d %>%
filter(mutations == "Order 2") %>%
ggplot(aes(x = idiosync)) +
geom_histogram(binwidth = 0.08, fill = "#edae49", color = "black", lwd = 0.1) +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(2), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(10), lty = 2, lwd = 0.2) +
labs(x = expression("2"*sigma),
y = "Genotype Count") +
scale_x_continuous(limits = c(0,5)) + #Scale that will be applied for all orders later
theme_classic() +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none")
## Warning: Removed 2 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
## Order 3
idio_d %>%
filter(mutations == "Order 3") %>%
ggplot(aes(x = idiosync)) +
geom_histogram(binwidth = 0.08, fill = "#edae49", color = "black", lwd = 0.1) +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(2), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(10), lty = 2, lwd = 0.2) +
labs(x = expression("2"*sigma),
y = "Genotype Count") +
#scale_x_continuous(limits = c(0,5)) + #Scale that will be applied for all orders later
theme_classic() +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none")
idio_d %>%
filter(mutations == "Order 4") %>%
ggplot(aes(x = idiosync)) +
geom_histogram(binwidth = 0.08, fill = "#edae49", color = "black", lwd = 0.1) +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(2), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(10), lty = 2, lwd = 0.2) +
labs(x = expression("2"*sigma),
y = "Genotype Count") +
scale_x_continuous(limits = c(0,5)) + #Scale that will be applied for all orders later
theme_classic() +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none")
## Warning: Removed 14 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
idio_d_multi_plot = idio_d %>%
filter(mutations != "Order 1" & mutations != "Order 5" & mutations != "Order 6") %>%
ggplot(aes(x = idiosync)) +
geom_histogram(binwidth = 0.2, color = "black", lwd = 0.1, fill = "#edae49") +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(2), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(10), lty = 2, lwd = 0.2) +
labs(x = expression("2"*sigma),
y = "Combination Count") +
facet_grid(~ mutations) +
scale_x_continuous(limits = c(0,5)) +
theme_classic() +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none")
idio_d_multi_plot # Data removed
## Warning: Removed 51 rows containing non-finite values (stat_bin).
## Warning: Removed 6 rows containing missing values (geom_bar).
#ggsave("fig_2A.svg", idio_d_multi_plot, width = 180, height = 247/4, dpi = 300, units = "mm")
What percentage of positions remain significantly heterogeneous (1.5-fold) at these orders?
idio_d %>%
filter(mutations == "Order 2" | mutations == "Order 3" | mutations == "Order 4") %>%
mutate(idiosync = idiosync > log10(1.5)) %>%
group_by(mutations) %>%
summarise(idiosync_sig = sum(idiosync),
idiosync_total = length(idiosync),
idiosync = sum(idiosync)/length(idiosync) * 100
)
## # A tibble: 3 × 4
## mutations idiosync_sig idiosync_total idiosync
## <chr> <int> <int> <dbl>
## 1 Order 2 413 419 98.6
## 2 Order 3 420 438 95.9
## 3 Order 4 240 245 98.0
What percentage of positions remain significantly heterogeneous (1.5-, 2-, 5-, and 10-fold) at all orders?
idio_d %>%
filter(mutations == "Order 1" | mutations == "Order 2" | mutations == "Order 3" | mutations == "Order 4") %>%
mutate(idiosync_1.5 = idiosync > log10(1.5),
idiosync_2 = idiosync > log10(2),
idiosync_5 = idiosync > log10(5),
idiosync_10 = idiosync > log10(10)) %>%
group_by(mutations) %>%
summarise(idiosync_sig_1.5 = sum(idiosync_1.5),
idiosync_sig_2 = sum(idiosync_2),
idiosync_sig_5 = sum(idiosync_5),
idiosync_sig_10 = sum(idiosync_10),
idiosync_total = length(idiosync),
idiosync_1.5 = sum(idiosync_1.5)/length(idiosync_1.5) * 100,
idiosync_2 = sum(idiosync_2)/length(idiosync_2) * 100,
idiosync_5 = sum(idiosync_5)/length(idiosync_5) * 100,
idiosync_10 = sum(idiosync_10)/length(idiosync_10) * 100
) %>%
knitr::kable()
| mutations | idiosync_sig_1.5 | idiosync_sig_2 | idiosync_sig_5 | idiosync_sig_10 | idiosync_total | idiosync_1.5 | idiosync_2 | idiosync_5 | idiosync_10 |
|---|---|---|---|---|---|---|---|---|---|
| Order 1 | 211 | 205 | 150 | 114 | 214 | 98.59813 | 95.79439 | 70.09346 | 53.27103 |
| Order 2 | 413 | 387 | 252 | 170 | 419 | 98.56802 | 92.36277 | 60.14320 | 40.57279 |
| Order 3 | 420 | 374 | 199 | 130 | 438 | 95.89041 | 85.38813 | 45.43379 | 29.68037 |
| Order 4 | 240 | 204 | 122 | 61 | 245 | 97.95918 | 83.26531 | 49.79592 | 24.89796 |
Collects all \(\Delta\)Function data for all orders
higher_order_list = list.files(path = "Output", pattern="higher_box", recursive = T)
higher_df = data.frame()
for(higher_file in 1:length(higher_order_list)){
Condition = str_split(str_split(higher_order_list[higher_file], "_")[[1]][3], "/")[[1]][1]
Measurement = str_split(higher_order_list[higher_file], "_")[[1]][2]
Enzyme = str_split(higher_order_list[higher_file], "_")[[1]][1]
appending_file = read_csv(paste0("Output/", higher_order_list[higher_file]), show_col_types=F)
appending_file$Condition = Condition
appending_file$Measurement = Measurement
appending_file$Enzyme = Enzyme
higher_df = bind_rows(higher_df, appending_file)
}
higher_df = higher_df %>%
unite("unique_id", c(pos, Condition, Measurement, Enzyme), remove = F) %>%
unite("partial_id", c(Enzyme, Measurement, Condition), remove = F)
higher_all_sign_check = higher_df %>%
filter(str_count(genotype, "x")/str_length(genotype) != 1) %>% # Remove interactions with no other backgrounds
mutate(mutations = factor(paste("Order", mutations, sep = " "))) %>%
group_by(unique_id, mutations) %>%
summarise(min_effect = min(avg),
max_effect = max(avg))
Assigning positive, neutral, negative, negative-neutral, positive-neutral, and negative-positive ‘types’ based on log10 1.5-fold threshold to every Order
threshold = 1.5
types = c()
for(each in 1:dim(higher_all_sign_check)[1]){
if(higher_all_sign_check$min_effect[each] < log10(1/threshold)){
# Negative branch
if(higher_all_sign_check$max_effect[each] < log10(1/threshold)) {
# Full negative
types = c(types, "Negative")
} else if(higher_all_sign_check$max_effect[each] <= log10(threshold)) {
# Negative Neutral
types = c(types, "Neutral Negative")
} else {
# Negative Positive
types = c(types, "Positive Negative")
}
} else {
# Neutral or Positive
if(higher_all_sign_check$min_effect[each] > log10(threshold)) {
# Full positive
types = c(types, "Positive")
} else if(higher_all_sign_check$max_effect[each] > log10(threshold)) {
# Neutral Positive
types = c(types, "Neutral Positive")
} else {
# Neutral
types = c(types, "Neutral")
}
}
}
higher_all_sign_check$type = factor(types)
All outputs show position/combination numbers in each category, followed by the total sum of all probed positions/combinations at that order, and finally percentages of each category
order_checker = higher_all_sign_check %>%
filter(mutations == "Order 1") %>%
pull(type)
order_checker_levels = c("Negative", "Neutral", "Positive", "Neutral Negative", "Neutral Positive", "Positive Negative")
summary(order_checker)
## Negative Neutral Neutral Negative Neutral Positive
## 3 2 16 35
## Positive Positive Negative
## 21 137
sum(summary(order_checker))
## [1] 214
summary(order_checker) / length(order_checker) * 100
## Negative Neutral Neutral Negative Neutral Positive
## 1.4018692 0.9345794 7.4766355 16.3551402
## Positive Positive Negative
## 9.8130841 64.0186916
pie_df = tibble(names = factor(names(summary(order_checker)), levels = order_checker_levels), values = summary(order_checker))
pie_order_1 = ggplot(pie_df, aes(x="", y=values, fill=names))+
geom_bar(width = 1, stat = "identity") +
coord_polar("y") +
theme_minimal() +
theme(axis.title.x = element_blank(), axis.title.y = element_blank(), panel.border = element_blank(), panel.grid=element_blank(), axis.ticks = element_blank(), plot.title=element_text(size=14, face="bold"))
pie_order_1
#ggsave("fig_1C.svg", pie_order_1, width = 180/2, height = 247/4, dpi = 300, units = "mm")
order_checker = higher_all_sign_check %>%
filter(mutations == "Order 2") %>%
pull(type)
summary(order_checker)
## Negative Neutral Neutral Negative Neutral Positive
## 5 5 37 64
## Positive Positive Negative
## 13 295
sum(summary(order_checker))
## [1] 419
summary(order_checker) / length(order_checker) * 100
## Negative Neutral Neutral Negative Neutral Positive
## 1.193317 1.193317 8.830549 15.274463
## Positive Positive Negative
## 3.102625 70.405728
pie_df = tibble(names = factor(names(summary(order_checker)), levels = order_checker_levels), values = summary(order_checker))
pie_order_2 = ggplot(pie_df, aes(x="", y=values, fill=names))+
geom_bar(width = 1, stat = "identity") +
coord_polar("y") +
theme_minimal() +
theme(axis.title.x = element_blank(), axis.title.y = element_blank(), panel.border = element_blank(), panel.grid=element_blank(), axis.ticks = element_blank(), plot.title=element_text(size=14, face="bold"))
pie_order_2
#ggsave("fig_2B_1.svg", pie_order_2, width = 180/2, height = 247/4, dpi = 300, units = "mm")
order_checker = higher_all_sign_check %>%
filter(mutations == "Order 3") %>%
pull(type)
summary(order_checker)
## Negative Neutral Neutral Negative Neutral Positive
## 16 10 64 59
## Positive Positive Negative
## 52 237
sum(summary(order_checker))
## [1] 438
summary(order_checker) / length(order_checker) * 100
## Negative Neutral Neutral Negative Neutral Positive
## 3.652968 2.283105 14.611872 13.470320
## Positive Positive Negative
## 11.872146 54.109589
pie_df = tibble(names = factor(names(summary(order_checker)), levels = order_checker_levels), values = summary(order_checker))
pie_order_3 = ggplot(pie_df, aes(x="", y=values, fill=names))+
geom_bar(width = 1, stat = "identity") +
coord_polar("y") +
theme_minimal() +
theme(axis.title.x = element_blank(), axis.title.y = element_blank(), panel.border = element_blank(), panel.grid=element_blank(), axis.ticks = element_blank(), plot.title=element_text(size=14, face="bold"))
pie_order_3
#ggsave("fig_2B_2.svg", pie_order_3, width = 180/2, height = 247/4, dpi = 300, units = "mm")
order_checker = higher_all_sign_check %>%
filter(mutations == "Order 4") %>%
pull(type)
summary(order_checker)
## Negative Neutral Neutral Negative Neutral Positive
## 11 5 50 42
## Positive Positive Negative
## 10 127
sum(summary(order_checker))
## [1] 245
summary(order_checker) / length(order_checker) * 100
## Negative Neutral Neutral Negative Neutral Positive
## 4.489796 2.040816 20.408163 17.142857
## Positive Positive Negative
## 4.081633 51.836735
pie_df = tibble(names = factor(names(summary(order_checker)), levels = order_checker_levels), values = summary(order_checker))
pie_order_4 = ggplot(pie_df, aes(x="", y=values, fill=names))+
geom_bar(width = 1, stat = "identity") +
coord_polar("y") +
theme_minimal() +
theme(axis.title.x = element_blank(), axis.title.y = element_blank(), panel.border = element_blank(), panel.grid=element_blank(), axis.ticks = element_blank(), plot.title=element_text(size=14, face="bold"))
pie_order_4
#ggsave("fig_2B_3.svg", pie_order_4, width = 180/2, height = 247/4, dpi = 300, units = "mm")
Raw Values
higher_all_sign_check_table = higher_all_sign_check %>%
group_by(mutations) %>%
count(type) %>%
pivot_wider(names_from = type,
values_from = n) %>%
mutate(Total = sum(across("Negative":"Positive Negative")))
higher_all_sign_check_table %>% knitr::kable()
| mutations | Negative | Neutral | Neutral Negative | Neutral Positive | Positive | Positive Negative | Total |
|---|---|---|---|---|---|---|---|
| Order 1 | 3 | 2 | 16 | 35 | 21 | 137 | 214 |
| Order 2 | 5 | 5 | 37 | 64 | 13 | 295 | 419 |
| Order 3 | 16 | 10 | 64 | 59 | 52 | 237 | 438 |
| Order 4 | 11 | 5 | 50 | 42 | 10 | 127 | 245 |
| Order 5 | 5 | 4 | 12 | 18 | 6 | 39 | 84 |
| Order 6 | 1 | 2 | 2 | 2 | 1 | 6 | 14 |
Percentages
higher_all_sign_check_table %>%
mutate(Negative = Negative/Total*100,
Neutral = Neutral/Total*100,
`Neutral Negative` = `Neutral Negative`/Total*100,
`Neutral Positive` = `Neutral Positive`/Total*100,
Positive = Positive/Total*100,
`Positive Negative` = `Positive Negative`/Total*100) %>%
knitr::kable()
| mutations | Negative | Neutral | Neutral Negative | Neutral Positive | Positive | Positive Negative | Total |
|---|---|---|---|---|---|---|---|
| Order 1 | 1.401869 | 0.9345794 | 7.476636 | 16.35514 | 9.813084 | 64.01869 | 214 |
| Order 2 | 1.193317 | 1.1933174 | 8.830549 | 15.27446 | 3.102625 | 70.40573 | 419 |
| Order 3 | 3.652968 | 2.2831050 | 14.611872 | 13.47032 | 11.872146 | 54.10959 | 438 |
| Order 4 | 4.489796 | 2.0408163 | 20.408163 | 17.14286 | 4.081633 | 51.83673 | 245 |
| Order 5 | 5.952381 | 4.7619048 | 14.285714 | 21.42857 | 7.142857 | 46.42857 | 84 |
| Order 6 | 7.142857 | 14.2857143 | 14.285714 | 14.28571 | 7.142857 | 42.85714 | 14 |
Purpose of this analysis was to determine the difference in \(\Delta\)Function between the WT-background contribution of a given position/combination and the mean contribution of a given position or combination
The spread of all positional contribution vs mean differences for each Order (1-4)
higher_df_mean = higher_df %>%
filter(mutations < 5) %>%
filter(str_count(genotype, "x")/str_length(genotype) != 1) %>% # Remove interactions with no other backgrounds
group_by(unique_id, partial_id) %>%
summarise(avg = mean(avg))
devs = c()
sign_devs = c()
cur_mutations = c()
for(i in 1:dim(higher_df_mean)[1]) {
curr_id = higher_df_mean$unique_id[i]
devs = c(devs, abs( (higher_df %>% filter(!str_detect(genotype, "1")) %>% filter(unique_id == curr_id) %>% pull(avg)) - (higher_df_mean$avg[i]) ))
cur_mutations = c(cur_mutations, (higher_df %>% filter(!str_detect(genotype, "1")) %>% filter(unique_id == curr_id) %>% pull(mutations)))
sign_devs = c(sign_devs, ifelse( (((higher_df %>% filter(!str_detect(genotype, "1")) %>% filter(unique_id == curr_id) %>% pull(avg)) > log10(1.5) & (higher_df_mean$avg[i]) < log10(1/1.5)) | ((higher_df %>% filter(!str_detect(genotype, "1")) %>% filter(unique_id == curr_id) %>% pull(avg)) < log10(1/1.5) & (higher_df_mean$avg[i]) > log10(1.5))), T, F)) ## Check if WT is positive while average is negative or vice versa
}
devs_df_wt = data.frame(devs = devs, muts = cur_mutations, sign_devs = sign_devs)
## Add unique ID if the WT deviates from mean
devs_df_wt = devs_df_wt %>%
mutate(unique_id = higher_df_mean$unique_id[row_number()]) %>%
mutate(partial_id = higher_df_mean$partial_id[row_number()])
How many WT points have sign deviation from the mean at the 1st order?
paste0(length(which(devs_df_wt %>% filter(muts == 1) %>% pull(sign_devs))), "/", length(devs_df_wt %>% filter(muts == 1) %>% pull(sign_devs)))
## [1] "7/214"
paste0(round(sum(devs_df_wt %>% filter(muts == 1) %>% pull(sign_devs)) / length(devs_df_wt %>% filter(muts == 1) %>% pull(sign_devs)) * 100, 2), "%")
## [1] "3.27%"
How many WT points have sign deviation from the mean at the 2nd order?
paste0(length(which(devs_df_wt %>% filter(muts == 2) %>% pull(sign_devs))), "/", length(devs_df_wt %>% filter(muts == 2) %>% pull(sign_devs)))
## [1] "53/419"
paste0(round(sum(devs_df_wt %>% filter(muts == 2) %>% pull(sign_devs)) / length(devs_df_wt %>% filter(muts == 2) %>% pull(sign_devs)) * 100, 2), "%")
## [1] "12.65%"
How many WT points have sign deviation from the mean at the 3rd order?
paste0(length(which(devs_df_wt %>% filter(muts == 3) %>% pull(sign_devs))), "/", length(devs_df_wt %>% filter(muts == 3) %>% pull(sign_devs)))
## [1] "34/438"
paste0(round(sum(devs_df_wt %>% filter(muts == 3) %>% pull(sign_devs)) / length(devs_df_wt %>% filter(muts == 3) %>% pull(sign_devs)) * 100, 2), "%")
## [1] "7.76%"
How many WT points have sign deviation from the mean at the 4th order?
paste0(length(which(devs_df_wt %>% filter(muts == 4) %>% pull(sign_devs))), "/", length(devs_df_wt %>% filter(muts == 4) %>% pull(sign_devs)))
## [1] "12/245"
paste0(round(sum(devs_df_wt %>% filter(muts == 4) %>% pull(sign_devs)) / length(devs_df_wt %>% filter(muts == 4) %>% pull(sign_devs)) * 100, 2), "%")
## [1] "4.9%"
Next we move away from sign to normal distance heterogenity
devs_df_wt_1_plot = devs_df_wt %>%
filter(muts == 1) %>%
ggplot(aes(x = devs)) +
geom_histogram(binwidth=0.08, color = "black", lwd = 0.1, fill = "#edae49") +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(2), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(10), lty = 2, lwd = 0.2) +
labs(x = expression("Deviation from Positional Mean ("*Delta*italic("F")*")"),
y = "Position Count") +
theme_classic() +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none")
devs_df_wt_1_plot
#ggsave("fig_1D.svg", devs_df_wt_1_plot, width = 180/2, height = 247/4, dpi = 300, units = "mm")
devs_df_wt %>%
filter(muts == 2) %>%
ggplot(aes(x = devs)) +
geom_histogram(binwidth=0.08, color = "black", lwd = 0.1, fill = "#edae49") +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(2), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(10), lty = 2, lwd = 0.2) +
theme_classic() +
scale_x_continuous(limits = c(0, 3)) +
labs(x = expression("Deviation from Combinatorial Mean ("*epsilon*")"),
y = "Combination Count") +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none")
## Warning: Removed 1 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
devs_df_wt %>%
filter(muts == 3) %>%
ggplot(aes(x = devs)) +
geom_histogram(binwidth=0.08, color = "black", lwd = 0.1, fill = "#edae49") +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(2), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(10), lty = 2, lwd = 0.2) +
scale_x_continuous(limits = c(0, 3)) +
theme_classic() +
labs(x = expression("Deviation from Combinatorial Mean ("*epsilon*")"),
y = "Combination Count") +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none")
## Warning: Removed 2 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
devs_df_wt %>%
filter(muts == 4) %>%
ggplot(aes(x = devs)) +
geom_histogram(binwidth=0.08, color = "black", lwd = 0.1, fill = "#edae49") +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(2), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(10), lty = 2, lwd = 0.2) +
theme_classic() +
scale_x_continuous(limits = c(0, 3)) + # Same scale used later
labs(x = expression("Deviation from Combinatorial Mean ("*epsilon*")"),
y = "Combination Count") +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none")
## Warning: Removed 5 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
devs_df_wt_multi = devs_df_wt %>%
filter(muts == 2 | muts == 3 | muts == 4) %>%
ggplot(aes(x = devs)) +
geom_histogram(binwidth=0.1, color = "black", lwd = 0.1, fill = "#edae49") +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(2), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(10), lty = 2, lwd = 0.2) +
facet_grid(~ muts) +
scale_x_continuous(limits = c(0, 3)) +
theme_classic() +
labs(x = expression("Deviation from Combinatorial Mean ("*epsilon*")"),
y = "Combination Count") +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none")
devs_df_wt_multi
## Warning: Removed 8 rows containing non-finite values (stat_bin).
## Warning: Removed 6 rows containing missing values (geom_bar).
#ggsave("fig_2C.svg", devs_df_wt_multi, width = 180, height = 247/4, dpi = 300, units = "mm")
Table to show percentages of WT-bg points that deviate from the positional/combinatorial mean by significance threshold (log10 1.5-fold)
devs_df_wt %>%
mutate(muts = factor(muts)) %>%
group_by(muts) %>%
summarise(percent = sum(devs > log10(1.5)) / length(devs),
outlier = sum(devs > log10(1.5)),
total = length(devs)) %>%
knitr::kable()
| muts | percent | outlier | total |
|---|---|---|---|
| 1 | 0.6775701 | 145 | 214 |
| 2 | 0.6443914 | 270 | 419 |
| 3 | 0.5570776 | 244 | 438 |
| 4 | 0.6244898 | 153 | 245 |
Table to show percentages of WT-bg points that deviate from the positional/combinatorial mean by multiple significance threshold (log10 1.5-, 2-, 5-, and 10-fold)
devs_df_wt %>%
mutate(Order = factor(muts)) %>%
group_by(Order) %>%
summarise(percent_1.5 = round(sum(devs > log10(1.5)) / length(devs) *100, 1),
outlier_1.5 = sum(devs > log10(1.5)),
percent_2 = round(sum(devs > log10(2)) / length(devs) *100, 1),
outlier_2 = sum(devs > log10(2)),
percent_5 = round(sum(devs > log10(5)) / length(devs) *100, 1),
outlier_5 = sum(devs > log10(5)),
percent_10 = round(sum(devs > log10(10)) / length(devs) *100, 1),
outlier_10 = sum(devs > log10(10)),
total = length(devs)) %>%
knitr::kable()
| Order | percent_1.5 | outlier_1.5 | percent_2 | outlier_2 | percent_5 | outlier_5 | percent_10 | outlier_10 | total |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 67.8 | 145 | 51.4 | 110 | 19.6 | 42 | 9.8 | 21 | 214 |
| 2 | 64.4 | 270 | 51.6 | 216 | 21.7 | 91 | 10.0 | 42 | 419 |
| 3 | 55.7 | 244 | 34.5 | 151 | 18.9 | 83 | 13.7 | 60 | 438 |
| 4 | 62.4 | 153 | 33.9 | 83 | 6.9 | 17 | 4.9 | 12 | 245 |
Purpose of this analysis was to determine the difference in \(\Delta\)Function between functional contribution of a given position/combination in any background and the mean contribution of a given position or combination
The spread of all positional contribution vs mean differences for each Order (1-4)
devs = c()
cur_mutations = c()
cur_partial_id = c()
for(i in 1:dim(higher_df_mean)[1]) {
curr_id = higher_df_mean$unique_id[i]
devs = c(devs, abs( (higher_df %>% filter(unique_id == curr_id) %>% pull(avg)) - (higher_df_mean$avg[i] ) ))
cur_mutations = c(cur_mutations, (higher_df %>% filter(unique_id == curr_id) %>% pull(mutations)))
cur_partial_id = c(cur_partial_id, (higher_df %>% filter(unique_id == curr_id) %>% pull(partial_id)))
}
devs_df = data.frame(devs = devs, muts = cur_mutations, partial_id = cur_partial_id)
devs_df %>%
filter(muts == 1) %>%
ggplot(aes(x = devs)) +
geom_histogram(binwidth=0.08, color = "black", lwd=0.1, fill = "#edae49") +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(2), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(10), lty = 2, lwd = 0.2) +
theme_classic() +
labs(x = expression("Deviation from Positional Mean ("*Delta*italic("F")*")"),
y = "Genotype Count") +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none")
devs_df %>%
filter(muts == 2) %>%
ggplot(aes(x = devs)) +
geom_histogram(binwidth=0.08, color = "black", lwd=0.1, fill = "#edae49") +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(2), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(10), lty = 2, lwd = 0.2) +
theme_classic() +
labs(x = expression("Deviation from Positional Mean ("*Delta*italic("F")*")"),
y = "Genotype Count") +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none")
devs_df %>%
filter(muts == 3) %>%
ggplot(aes(x = devs)) +
geom_histogram(binwidth=0.08, color = "black", lwd=0.1, fill = "#edae49") +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(2), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(10), lty = 2, lwd = 0.2) +
theme_classic() +
labs(x = expression("Deviation from Positional Mean ("*Delta*italic("F")*")"),
y = "Genotype Count") +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none")
devs_df %>%
filter(muts == 4) %>%
ggplot(aes(x = devs)) +
geom_histogram(binwidth=0.08, color = "black", lwd=0.1, fill = "#edae49") +
geom_vline(xintercept = log10(1.5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(2), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(5), lty = 2, lwd = 0.2) +
geom_vline(xintercept = log10(10), lty = 2, lwd = 0.2) +
theme_classic() +
labs(x = expression("Deviation from Positional Mean ("*Delta*italic("F")*")"),
y = "Genotype Count") +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none")
Table to show percentages of WT-bg points that deviate from the positional/combinatorial mean by significance threshold (log10 1.5-fold)
devs_df %>%
mutate(Order = factor(muts)) %>%
group_by(Order) %>%
summarise(percent = sum(devs > log10(1.5)) / length(devs),
outlier = sum(devs > log10(1.5)),
total = length(devs)) %>%
knitr::kable()
| Order | percent | outlier | total |
|---|---|---|---|
| 1 | 0.5895669 | 2396 | 4064 |
| 2 | 0.5278253 | 2466 | 4672 |
| 3 | 0.4929435 | 1467 | 2976 |
| 4 | 0.5598214 | 627 | 1120 |
Table to show percentages of WT-bg points that deviate from the positional/combinatorial mean by many significance thresholds (log10 1.5-, 2-, 5-, and 10-fold)
devs_df %>%
mutate(Order = factor(muts)) %>%
group_by(Order) %>%
summarise(percent_1.5 = sum(devs > log10(1.5)) / length(devs),
outlier_1.5 = sum(devs > log10(1.5)),
percent_2 = sum(devs > log10(2)) / length(devs),
outlier_2 = sum(devs > log10(2)),
percent_5 = sum(devs > log10(5)) / length(devs),
outlier_5 = sum(devs > log10(5)),
percent_10 = sum(devs > log10(10)) / length(devs),
outlier_10 = sum(devs > log10(10)),
total = length(devs)) %>%
knitr::kable()
| Order | percent_1.5 | outlier_1.5 | percent_2 | outlier_2 | percent_5 | outlier_5 | percent_10 | outlier_10 | total |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.5895669 | 2396 | 0.4050197 | 1646 | 0.1478839 | 601 | 0.0814469 | 331 | 4064 |
| 2 | 0.5278253 | 2466 | 0.3488870 | 1630 | 0.1335616 | 624 | 0.0766267 | 358 | 4672 |
| 3 | 0.4929435 | 1467 | 0.3067876 | 913 | 0.1159274 | 345 | 0.0762769 | 227 | 2976 |
| 4 | 0.5598214 | 627 | 0.3455357 | 387 | 0.0901786 | 101 | 0.0500000 | 56 | 1120 |
This sections attempts to probe if the previous values from the statistical analysis are representative in the individual data sets
d_individual = d %>% filter_all(any_vars(!is.na(.))) # removes rows with all NA before making unique ID
d_individual = d_individual %>%
unite("landscape_id", c(enzyme, type, cond), remove = F)
landscape_ids = unique(d_individual$landscape_id)
general_df = tibble()
for(i in 1:length(landscape_ids)) {
d_curr_individual = d_individual %>% filter(landscape_id == landscape_ids[i])
general = c(unique(d_curr_individual$enzyme),
landscape_ids[i],
sum(d_curr_individual$effects < log10(1/1.5)) / length(d_curr_individual$effects),
sum(d_curr_individual$effects <= log10(1.5) & d_curr_individual$effects >= log10(1/1.5)) / length(d_curr_individual$effects),
sum(d_curr_individual$effects > log10(1.5)) / length(d_curr_individual$effects)
)
general_df = rbind(general_df, general)
}
colnames(general_df) = c("Enzyme", "Landscape ID", "Negative", "Neutral", "Positive")
general_df[-1]
## Landscape ID Negative Neutral Positive
## 1 AP_catef_1 0.775 0.075 0.15
## 2 DHFR_ic50_c57 0.375 0.09375 0.53125
## 3 DHFR_ic50_c58 0.375 0.09375 0.53125
## 4 DHFR_ic50_c59 0.46875 0.15625 0.375
## 5 DHFR_ic50_c60 0.375 0.21875 0.40625
## 6 DHFR_ic50_c61 0.125 0.3125 0.5625
## 7 DHFR_ic75_palmer 0.223958333333333 0.25 0.526041666666667
## 8 DHFR_kcat_trajg 0.6625 0.2 0.1375
## 9 DHFR_kcat_trajr 0.5875 0.2875 0.125
## 10 DHFR_ki_trajg 0 0 1
## 11 DHFR_ki_trajr 0 0 1
## 12 MPH_catact_CaPTM 0.15 0.1875 0.6625
## 13 MPH_catact_CdPTM 0.1375 0.3375 0.525
## 14 MPH_catact_CoPTM 0.2 0.0875 0.7125
## 15 MPH_catact_CuPTM 0.225 0.1625 0.6125
## 16 MPH_catact_MgPTM 0.2125 0.25 0.5375
## 17 MPH_catact_MnPTM 0.25 0.1875 0.5625
## 18 MPH_catact_NiPTM 0.2 0.1625 0.6375
## 19 MPH_catact_ZnPTM 0.2 0.175 0.625
## 20 NfsA_ec50_2039 0.09375 0.627232142857143 0.279017857142857
## 21 NfsA_ec50_3637 0.0982142857142857 0.529017857142857 0.372767857142857
## 22 OXA-48_ic50_CAZtraj1 0 0.4375 0.5625
## 23 OXA-48_ic50_CAZtraj2 0.0729166666666667 0.333333333333333 0.59375
## 24 OXA-48_ic50_CAZtraj3 0.03125 0.677083333333333 0.291666666666667
## 25 OXA-48_ic50_PIPtraj1 0.375 0.375 0.25
## 26 OXA-48_ic50_PIPtraj2 0.369791666666667 0.473958333333333 0.15625
## 27 OXA-48_ic50_PIPtraj3 0.3125 0.583333333333333 0.104166666666667
## 28 PTE_catact_2NH 0.0885416666666667 0.239583333333333 0.671875
## 29 PTE_catact_butyrate 0.239583333333333 0.317708333333333 0.442708333333333
## 30 TEM_growth_AM 0.3125 0.3125 0.375
## 31 TEM_growth_AMC 0.28125 0.40625 0.3125
## 32 TEM_growth_AMP 0.3125 0.40625 0.28125
## 33 TEM_growth_CAZ 0.28125 0.25 0.46875
## 34 TEM_growth_CEC 0.40625 0.28125 0.3125
## 35 TEM_growth_CPD 0.25 0.1875 0.5625
## 36 TEM_growth_CPR 0.4375 0.40625 0.15625
## 37 TEM_growth_CRO 0.375 0.09375 0.53125
## 38 TEM_growth_CTT 0.4375 0.0625 0.5
## 39 TEM_growth_CTX 0.1875 0.46875 0.34375
## 40 TEM_growth_CXM 0.28125 0.21875 0.5
## 41 TEM_growth_FEP 0.21875 0.25 0.53125
## 42 TEM_growth_SAM 0.125 0.3125 0.5625
## 43 TEM_growth_TZP 0.5625 0.21875 0.21875
## 44 TEM_growth_ZOX 0.25 0.3125 0.4375
## 45 TEM_MIC_weinreich 0.0375 0.3 0.6625
The trend appears to be heterogenous, i.e. the fraction of negative, neutral, and positive is varied across all landscapes but averages out to roughly equal proportions when all landscapes are considered together.
For example, AP shows very negative contribution at 77.5% of all mutations, DHFR kcats are predominantly negative while Kis are almost all positive (due to the adaptive nature of the mutations). MPH shows strong positives as it is also adaptive. NfsA is predominantly neutral, with some positive contribution. OXA is positive and neutral for adaptive trajectories, and negative and neutral for trade-off. PTE is positive for its adaptive substrate, but shows more negative behavior for lactones and OPs. TEM across all antibiotics is predominantly neutral (no directy adaptation). Finally the adaptive MIC of TEM is mostly positive with 66.3%.
general_df %>%
pivot_longer(c(3,4,5)) %>%
mutate(value = as.numeric(value)*100) %>%
ggplot(aes(x = name, y = value, color = Enzyme)) +
geom_point() +
stat_summary(fun=mean, colour="darkred", geom="crossbar", width=0.2) +
theme_classic()
all_mae = lapply(paste0("Output/", list.files(path = "Output", pattern="aic_*", recursive = T)), read_csv, skip = 1, show_col_types=F, col_names = c("MAE", "Last MAE", "Step", "Enzyme", "Linear MAE", "Linear Last MAE"))
for(element in 1:length(all_mae)){
all_mae[[element]]$ID = str_split(list.files(path = "Output", pattern="aic_*", recursive = T)[element], "/")[[1]][1]
}
all_mae = do.call("rbind", all_mae)
Some statistics for the AE for each model. Now F is converted to fold-change in function (but remains WT-normalized)
all_mae %>%
group_by(Step) %>%
summarise(wtbg_mean = 10^mean(`Last MAE`),
wtbg_median = 10^median(`Last MAE`),
lin_mean = 10^mean(`Linear Last MAE`),
lin_median = 10^median(`Linear Last MAE`)) %>%
knitr::kable()
| Step | wtbg_mean | wtbg_median | lin_mean | lin_median |
|---|---|---|---|---|
| 1 | 20.531688 | 8.008615 | 2.945869 | 2.302982 |
| 2 | 106.163799 | 63.740988 | 2.023382 | 1.765125 |
| 3 | 51.067872 | 8.952979 | 1.267406 | 1.183051 |
| 4 | 19.699102 | 3.151889 | 1.108533 | 1.073554 |
| 5 | 7.322787 | 1.313971 | 1.094891 | 1.080794 |
| 6 | 1.730950 | 1.000000 | 1.114261 | 1.106314 |
| 7 | 1.000000 | 1.000000 | 1.087920 | 1.087920 |
After loading and assigning ID corresponding to each landscape value (should be 45) we can plot linear and WT-model MAE’s with their constituents
all_mae_long = all_mae %>%
pivot_longer(cols = c('Last MAE', 'Linear Last MAE'), names_to = "Model", values_to = "AE") %>%
filter(Step < 5)
mae_ggplot = all_mae_long %>%
ggplot(aes(x = Step, y = AE, color = Model)) +
geom_point(size = 1, position=position_jitterdodge(jitter.width = 0.1)) +
stat_summary(data = all_mae_long %>% filter(Model == "Last MAE"), fun=mean, colour="black", geom="crossbar", width=0.2, position = position_nudge(x = -0.19, y = 0), size = 0.3) +
stat_summary(data = all_mae_long %>% filter(Model == "Linear Last MAE"), fun=mean, colour="black", geom="crossbar", width=0.2, position = position_nudge(x = 0.19, y = 0),size = 0.3) +
stat_summary(data = all_mae_long %>% filter(Model == "Last MAE"), fun=median, colour="darkred", geom="crossbar", width=0.2, position = position_nudge(x = -0.19, y = 0), size = 0.3) +
stat_summary(data = all_mae_long %>% filter(Model == "Linear Last MAE"), fun=median, colour="darkred", geom="crossbar", width=0.2, position = position_nudge(x = 0.19, y = 0),size = 0.3) +
geom_hline(yintercept = log10(1.5), lty = 2, size = 0.2) +
scale_color_manual(values = c("#edae49", "#4988ed")) +
labs(x = "Order of the Model",
y = expression("Absolute Error of Predicted"*italic(" F"))) +
theme_classic() +
theme(axis.line = element_line(size = 0.2, color = "black"), axis.ticks = element_line(size = 0.2, color = "black"), text = element_text(size = 9), axis.text = element_text(size = 8, color = "black"), legend.position = "none") +
coord_cartesian(ylim=c(0, 6)) # Zoom without affecting means and medians
mae_ggplot
#ggsave("fig_3B.svg", mae_ggplot, width = 180/2, height = 247/4, dpi = 300, units = "mm")
This plot omits some data from being viewed, however plotted meeans and medians are accurate. The figure is eddited outside of the code for technical
What is the distribution of predicted vs non-predicted at each order, not just the means?
all_mae_long %>%
group_by(Step, Model) %>%
summarise(pred= length(AE[AE < log10(1.5)]),
non_pred= length(AE[AE >= log10(1.5)])) %>%
mutate(pred_ratio = round(pred/(pred+non_pred)*100,2)) %>%
knitr::kable()
## `summarise()` has grouped output by 'Step'. You can override using the `.groups` argument.
| Step | Model | pred | non_pred | pred_ratio |
|---|---|---|---|---|
| 1 | Last MAE | 6 | 39 | 13.33 |
| 1 | Linear Last MAE | 13 | 32 | 28.89 |
| 2 | Last MAE | 5 | 40 | 11.11 |
| 2 | Linear Last MAE | 15 | 30 | 33.33 |
| 3 | Last MAE | 5 | 40 | 11.11 |
| 3 | Linear Last MAE | 34 | 11 | 75.56 |
| 4 | Last MAE | 4 | 21 | 16.00 |
| 4 | Linear Last MAE | 24 | 1 | 96.00 |
First I defined the adaptive trajectories. These are hardcoded based on the dataset I am working with.
adaptive_trajectories = c("DHFR_ki_trajg",
"DHFR_ki_trajr",
"MPH_catact_ZnPTM",
"NfsA_ec50_2039",
"NfsA_ec50_3637",
"OXA-48_ic50_CAZtraj1",
"OXA-48_ic50_CAZtraj2",
"OXA-48_ic50_CAZtraj3",
"PTE_catact_2NH",
"TEM_MIC_weinreich",
"DHFR_ic75_palmer")
Then I need to create a global trajectory data frame
all_traj = do.call("rbind", lapply(paste0("Output/", list.files(path = "Output", pattern="traj_epi_*", recursive = T)), read_csv, skip = 1, show_col_types=F, col_names = c("genotype", "avg", "pos", "mutations", "likely", "enzyme_name", "measure_type", "cond")))
all_traj = all_traj %>% unite("unique_id", c(enzyme_name, measure_type, cond), remove = F)
Next I need to filter landscapes by whether they are adaptive or not
adaptive_traj = all_traj %>% filter(unique_id %in% adaptive_trajectories)
Which genotypes in the adaptive landscapes for the most accessible trajectory, as defined by the boolean variable likely, are POSITIVELY epistatic and epistatic in general?
Note: some trajectories don’t go to the last variant, so we explore epistasis of all other genotypes which may go beyond last variant
adaptive_traj_favored = adaptive_traj %>% filter(mutations > 1 & likely) %>% pull(avg)
sum(adaptive_traj_favored > log10(1.5)) / length(adaptive_traj_favored)
## [1] 0.3809524
sum(adaptive_traj_favored > log10(1.5) | adaptive_traj_favored < log10(1/1.5)) / length(adaptive_traj_favored)
## [1] 0.6428571
How many positive epistatic vs all epistatic genotypes vs all genotypes encountered in these landscapes?
sum(adaptive_traj_favored > log10(1.5))
## [1] 16
sum(adaptive_traj_favored > log10(1.5) | adaptive_traj_favored < log10(1/1.5))
## [1] 27
length(adaptive_traj_favored)
## [1] 42
Which genotypes in the non-adaptive landscapes for the favored trajectory are NEGATIVELY epistatic and epistatic in general? Also total genotypes that are not most accessible, with percentages of negative epistasis and epistasis in general.
`%notin%` <- Negate(`%in%`)
non_adaptive_traj_favored = all_traj %>% filter(unique_id %notin% adaptive_trajectories & mutations > 1 & !likely) %>% pull(avg)
sum(non_adaptive_traj_favored < log10(1/1.5))
## [1] 204
sum(non_adaptive_traj_favored > log10(1.5) | non_adaptive_traj_favored < log10(1/1.5))
## [1] 408
length(non_adaptive_traj_favored)
## [1] 557
sum(non_adaptive_traj_favored < log10(1/1.5)) / length(non_adaptive_traj_favored)
## [1] 0.3662478
sum(non_adaptive_traj_favored > log10(1.5) | non_adaptive_traj_favored < log10(1/1.5)) / length(non_adaptive_traj_favored)
## [1] 0.7324955
We construct thee plotting_d tibble which has information about the observed effect, linear model effect which is simply ccalled epistatic effect and the effect of each order of the WT-background model
adaptive_traj_fave = adaptive_traj %>% filter(likely)
###
plotting_d = tibble()
dummy2 = data.frame()
for(i in 1:length(adaptive_trajectories)) {
suffix = tail(str_split(adaptive_trajectories[i], "_")[[1]], 1)
file = paste0("pred_df_", suffix, "_", adaptive_trajectories[i], ".csv")
d = read_csv(paste0("Output/", adaptive_trajectories[i], "/", file), show_col_types=F)
genos = str_replace_all(adaptive_traj_fave[which(adaptive_traj_fave$unique_id %in% adaptive_trajectories[i]),]$genotype, "x", "1")
genos = c(str_replace_all(genos[1], "1", "0"), genos)
dummy2 = rbind(dummy2, data.frame(trajectory = adaptive_trajectories[i], Z = d[which(d$numbers == tail(genos, 1)),]$`observed effect`))
others = c()
for(j in 1:(length(genos) - 2)) {
others = c(others, colnames(d)[which(colnames(d) == "mutations") + j]) ## Get all steps before final step of evaluated genotype
}
plotting_d = rbind(plotting_d, d[which(d$numbers %in% genos),] %>%
pivot_longer(cols = c(`observed effect`,
`epistatic effect`,
`WT effect`,
others)) %>% ## add those steps into line plots
select(c(mutations, name, value)) %>%
mutate(trajectory = adaptive_trajectories[i]))
}
Next we plot all of those
ggplot(plotting_d, aes(x = mutations, y = value, color = name)) +
geom_line(lwd = 2) +
facet_wrap(~ trajectory) +
geom_hline(data = dummy2, aes(yintercept = Z)) +
geom_hline(data = dummy2, aes(yintercept = Z + log10(1.5)), lty = 2) +
geom_hline(data = dummy2, aes(yintercept = Z - log10(1.5)), lty = 2) +
geom_hline(data = dummy2, aes(yintercept = Z + log10(10)), lty = 2, color = "grey") +
geom_hline(data = dummy2, aes(yintercept = Z - log10(10)), lty = 2, color = "grey") +
theme_classic()
Also return as a table
plotting_d %>%
pivot_wider(names_from = name, values_from = value) %>%
knitr::kable()
| mutations | trajectory | observed effect | epistatic effect | WT effect | 2 step | 3 step | 4 step | final step | 5 step | 6 step |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | DHFR_ki_trajg | 0.0000000 | -0.0124266 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | NA | NA |
| 1 | DHFR_ki_trajg | 1.1303338 | 1.1421598 | 1.1303338 | 1.1303338 | 1.1303338 | 1.1303338 | 1.1303338 | NA | NA |
| 2 | DHFR_ki_trajg | 2.3598355 | 2.3480971 | 1.7504698 | 2.3598355 | 2.3598355 | 2.3598355 | 2.3598355 | NA | NA |
| 3 | DHFR_ki_trajg | 3.3541084 | 3.3664861 | 2.0928925 | 3.3782593 | 3.3541084 | 3.3541084 | 3.3541084 | NA | NA |
| 4 | DHFR_ki_trajg | 4.5820634 | 4.5691010 | 2.4392455 | 6.4837389 | 4.2208458 | 4.5820634 | 4.5820634 | NA | NA |
| 5 | DHFR_ki_trajg | 5.9656720 | 5.9782807 | 2.7914280 | 9.0023947 | 3.9479878 | 6.3680742 | 5.9656720 | NA | NA |
| 0 | DHFR_ki_trajr | 0.0000000 | -0.0155046 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | NA | NA |
| 1 | DHFR_ki_trajr | 1.1303338 | 1.1452378 | 1.1303338 | 1.1303338 | 1.1303338 | 1.1303338 | 1.1303338 | NA | NA |
| 2 | DHFR_ki_trajr | 2.3598355 | 2.3450191 | 1.7504698 | 2.3598355 | 2.3598355 | 2.3598355 | 2.3598355 | NA | NA |
| 3 | DHFR_ki_trajr | 3.3541084 | 3.3695641 | 2.0928925 | 3.3782593 | 3.3541084 | 3.3541084 | 3.3541084 | NA | NA |
| 4 | DHFR_ki_trajr | 4.2810334 | 4.2664918 | 2.4450750 | 5.1111507 | 4.4545256 | 4.2810334 | 4.2810334 | NA | NA |
| 5 | DHFR_ki_trajr | 5.2304489 | 5.2453065 | 2.8049105 | 7.8594519 | 4.0646531 | 5.7290199 | 5.2304489 | NA | NA |
| 0 | MPH_catact_ZnPTM | 0.0000000 | -0.0016066 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | NA | NA |
| 1 | MPH_catact_ZnPTM | 0.9943172 | 0.9914272 | 0.9943172 | 0.9943172 | 0.9943172 | 0.9943172 | 0.9943172 | NA | NA |
| 2 | MPH_catact_ZnPTM | 1.2068259 | 1.2012842 | 1.2661588 | 1.2068259 | 1.2068259 | 1.2068259 | 1.2068259 | NA | NA |
| 3 | MPH_catact_ZnPTM | 1.2405492 | 1.2353742 | 1.9345447 | 1.2404054 | 1.2405492 | 1.2405492 | 1.2405492 | NA | NA |
| 4 | MPH_catact_ZnPTM | 2.3117539 | 2.3103613 | 2.8654937 | 0.4136588 | 2.0807635 | 2.3117539 | 2.3117539 | NA | NA |
| 5 | MPH_catact_ZnPTM | 2.9609462 | 2.9605514 | 2.0776813 | -0.4173805 | 2.1149224 | 3.6596868 | 2.9609462 | NA | NA |
| 0 | NfsA_ec50_2039 | 0.0000000 | -0.0052604 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | NA | 0.0000000 | NA |
| 1 | NfsA_ec50_2039 | 0.4216039 | 0.4174507 | 0.4216039 | 0.4216039 | 0.4216039 | 0.4216039 | NA | 0.4216039 | NA |
| 2 | NfsA_ec50_2039 | 0.6190933 | 0.6108143 | 0.4785088 | 0.6190933 | 0.6190933 | 0.6190933 | NA | 0.6190933 | NA |
| 3 | NfsA_ec50_2039 | 0.7450748 | 0.7388571 | 0.4244695 | 0.7362056 | 0.7450748 | 0.7450748 | NA | 0.7450748 | NA |
| 4 | NfsA_ec50_2039 | 0.8488047 | 0.8477300 | 0.4121357 | 0.7618568 | 0.7740661 | 0.8488047 | NA | 0.8488047 | NA |
| 5 | NfsA_ec50_2039 | 0.9845273 | 0.9797227 | 0.3801515 | -0.1849312 | 1.2563700 | 1.2151156 | NA | 0.9845273 | NA |
| 0 | NfsA_ec50_3637 | 0.0000000 | -0.0052604 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 |
| 1 | NfsA_ec50_3637 | 0.4800069 | 0.4741952 | 0.4800069 | 0.4800069 | 0.4800069 | 0.4800069 | 0.4800069 | 0.4800069 | 0.4800069 |
| 2 | NfsA_ec50_3637 | 0.6711728 | 0.6693158 | 0.5369118 | 0.6711728 | 0.6711728 | 0.6711728 | 0.6711728 | 0.6711728 | 0.6711728 |
| 3 | NfsA_ec50_3637 | 0.8413595 | 0.8383239 | 0.5245781 | 0.6711856 | 0.8413595 | 0.8413595 | 0.8413595 | 0.8413595 | 0.8413595 |
| 4 | NfsA_ec50_3637 | 0.9180303 | 0.9137495 | 0.4462645 | 0.4576674 | 1.4626969 | 0.9180303 | 0.9180303 | 0.9180303 | 0.9180303 |
| 5 | NfsA_ec50_3637 | 0.9566486 | 0.9519493 | 0.1443651 | 0.8355329 | 1.8320479 | 0.0426191 | 0.9566486 | 0.9566486 | 0.9566486 |
| 6 | NfsA_ec50_3637 | 0.9454686 | 0.9436716 | 0.0727609 | 0.6151170 | 2.4772843 | -0.4757923 | 0.9454686 | 1.6847688 | 0.9454686 |
| 7 | NfsA_ec50_3637 | 1.0043214 | 1.0028427 | 0.0939502 | 1.2003309 | 1.7113959 | -1.2265483 | 1.0043214 | 4.5060330 | -0.9087341 |
| 0 | OXA-48_ic50_CAZtraj1 | 0.0000000 | -0.0026570 | 0.0000000 | 0.0000000 | 0.0000000 | NA | 0.0000000 | NA | NA |
| 1 | OXA-48_ic50_CAZtraj1 | 0.3560259 | 0.3488146 | 0.3560259 | 0.3560259 | 0.3560259 | NA | 0.3560259 | NA | NA |
| 2 | OXA-48_ic50_CAZtraj1 | 1.1398791 | 1.1223656 | 0.3360292 | 1.1398791 | 1.1398791 | NA | 1.1398791 | NA | NA |
| 3 | OXA-48_ic50_CAZtraj1 | 1.4842998 | 1.4833406 | 0.4115762 | 2.0345134 | 1.4842998 | NA | 1.4842998 | NA | NA |
| 4 | OXA-48_ic50_CAZtraj1 | 1.6042261 | 1.5994638 | 0.5255195 | 1.9259530 | 1.3318026 | NA | 1.6042261 | NA | NA |
| 0 | OXA-48_ic50_CAZtraj2 | 0.0000000 | -0.0026570 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | NA |
| 1 | OXA-48_ic50_CAZtraj2 | 0.3856063 | 0.3839493 | 0.3856063 | 0.3856063 | 0.3856063 | 0.3856063 | 0.3856063 | 0.3856063 | NA |
| 2 | OXA-48_ic50_CAZtraj2 | 0.8027737 | 0.7969589 | 0.7694216 | 0.8027737 | 0.8027737 | 0.8027737 | 0.8027737 | 0.8027737 | NA |
| 3 | OXA-48_ic50_CAZtraj2 | 1.0934217 | 1.0916060 | 0.9093007 | 1.4373797 | 1.0934217 | 1.0934217 | 1.0934217 | 1.0934217 | NA |
| 4 | OXA-48_ic50_CAZtraj2 | 1.5276299 | 1.5256020 | 0.9005268 | 2.1488613 | 1.1459227 | 1.5276299 | 1.5276299 | 1.5276299 | NA |
| 5 | OXA-48_ic50_CAZtraj2 | 1.5682017 | 1.5672424 | 0.8881931 | 2.2688061 | 1.6984605 | 1.6998067 | 1.5682017 | 1.5682017 | NA |
| 6 | OXA-48_ic50_CAZtraj2 | 1.5078559 | 1.5046215 | 0.8967932 | 1.9530868 | 0.7115869 | 2.2277266 | 1.5078559 | 1.2706843 | NA |
| 0 | OXA-48_ic50_CAZtraj3 | 0.0000000 | -0.0026570 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | NA | NA | NA |
| 1 | OXA-48_ic50_CAZtraj3 | 0.4329693 | 0.4292205 | 0.4329693 | 0.4329693 | 0.4329693 | 0.4329693 | NA | NA | NA |
| 2 | OXA-48_ic50_CAZtraj3 | 0.9938769 | 0.9737267 | 0.4898741 | 0.9938769 | 0.9938769 | 0.9938769 | NA | NA | NA |
| 3 | OXA-48_ic50_CAZtraj3 | 1.3364597 | 1.3340956 | 0.4941955 | 1.1216466 | 1.3364597 | 1.3364597 | NA | NA | NA |
| 4 | OXA-48_ic50_CAZtraj3 | 1.4149733 | 1.4081219 | 0.5355882 | 0.9184096 | 1.6022940 | 1.4149733 | NA | NA | NA |
| 0 | PTE_catact_2NH | 0.0000000 | -0.0030856 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | NA | NA | NA |
| 1 | PTE_catact_2NH | 1.4199557 | 1.4175792 | 1.4199557 | 1.4199557 | 1.4199557 | 1.4199557 | NA | NA | NA |
| 2 | PTE_catact_2NH | 2.2095150 | 2.2072984 | 2.2408137 | 2.2095150 | 2.2095150 | 2.2095150 | NA | NA | NA |
| 3 | PTE_catact_2NH | 3.0211893 | 3.0194333 | 2.8546556 | 4.0243530 | 3.0211893 | 3.0211893 | NA | NA | NA |
| 4 | PTE_catact_2NH | 3.2405492 | 3.2383995 | 2.6608355 | 4.1859390 | 2.3618565 | 3.2405492 | NA | NA | NA |
| 0 | TEM_MIC_weinreich | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | NA | NA |
| 1 | TEM_MIC_weinreich | 1.2013971 | 1.2016454 | 1.2013971 | 1.2013971 | 1.2013971 | 1.2013971 | 1.2013971 | NA | NA |
| 2 | TEM_MIC_weinreich | 3.6117233 | 3.6118198 | 1.3803741 | 3.6117233 | 3.6117233 | 3.6117233 | 3.6117233 | NA | NA |
| 3 | TEM_MIC_weinreich | 4.3783979 | 4.3777366 | 1.3803741 | 5.8493869 | 4.3783979 | 4.3783979 | 4.3783979 | NA | NA |
| 4 | TEM_MIC_weinreich | 4.5185139 | 4.5179153 | 1.2893949 | 8.6829825 | 2.5918364 | 4.5185139 | 4.5185139 | NA | NA |
| 5 | TEM_MIC_weinreich | 4.6683859 | 4.6683012 | 1.2893949 | 8.9067386 | 3.4005435 | 4.3989125 | 4.6683859 | NA | NA |
| 0 | DHFR_ic75_palmer | 0.0000000 | -0.0177247 | 0.0000000 | 0.0000000 | 0.0000000 | NA | NA | NA | NA |
| 1 | DHFR_ic75_palmer | 1.8555192 | 1.8548586 | 1.8555192 | 1.8555192 | 1.8555192 | NA | NA | NA | NA |
| 2 | DHFR_ic75_palmer | 2.1931246 | 2.1911919 | 2.1630152 | 2.1931246 | 2.1931246 | NA | NA | NA | NA |
| 3 | DHFR_ic75_palmer | 2.3424227 | 2.3418586 | 2.8064679 | 2.4667779 | 2.3424227 | NA | NA | NA | NA |
I can make a ruggedness plot for each adaptive landscape, where deviation from 0 implies epistatic contribution of that genotype
adaptive_traj %>%
filter(mutations > 1 & likely) %>%
ggplot(aes(x = mutations, y = avg)) +
geom_line() +
geom_text_repel(aes(label = genotype), force = 1.5, direction = "y", box.padding = 0, segment.size = 0.2, size = 2.5) +
geom_hline(yintercept = log10(1.5), lty = 2) +
geom_hline(yintercept = log10(1/1.5), lty = 2) +
geom_hline(yintercept = 0) +
facet_wrap(~ unique_id) +
theme_classic()
What if we compare it to the mean at that combination or position as well, not just assumed additivity (0 +/- log10(1.5))?
higher_means = higher_df %>%
unite("partial_id", c(Enzyme, Measurement, Condition), remove = F) %>%
filter(partial_id %in% adaptive_trajectories) %>% ## use partial ID to only pick up the adaptive trajectories
group_by(unique_id) %>%
mutate(avg = mean(avg)) %>% ## Is arithmetic mean OK here?
ungroup() %>%
distinct(unique_id, .keep_all = T) ## Remove unique_id replicates
adaptive_means = higher_means %>% arrange(partial_id, pos) %>% pull(avg)
adaptive_traj = adaptive_traj %>% arrange(unique_id, pos) %>% mutate(means = adaptive_means)
Then we make the plot
trajectory_labeller = function(variable,value){
return(trajectory_names[value])
}
trajectory_names = list(
"DHFR_ic75_palmer" = expression("DHFR Palmer"*italic(" et al.")),
"DHFR_ki_trajg" = expression("DHFR Traj. G"),
"DHFR_ki_trajr" = expression("DHFR Traj. R"),
"MPH_catact_ZnPTM" = expression("MPH Traj. Zn"),
"NfsA_ec50_2039" = expression("NfsA Traj. 20_39"),
"NfsA_ec50_3637" = expression("NfsA Traj. 36_37"),
"OXA-48_ic50_CAZtraj1" = expression("OXA-48 CAZ Traj. 1"),
"OXA-48_ic50_CAZtraj2" = expression("OXA-48 CAZ Traj. 2"),
"OXA-48_ic50_CAZtraj3" = expression("OXA-48 CAZ Traj. 3"),
"PTE_catact_2NH" = expression("PTE Traj. 2NH"),
"TEM_MIC_weinreich" = expression("TEM Weinreich"*italic(" et al."))
)
adaptive_traj_plot = adaptive_traj %>%
filter(likely & mutations > 1) %>%
mutate(wt_bg = avg) %>%
pivot_longer(c(wt_bg, means)) %>%
ggplot(aes(x = factor(mutations), y = value, fill = name)) +
geom_bar(stat = "identity", position = position_dodge()) +
#geom_text_repel(aes(label = genotype), force = 1.5, direction = "y", box.padding = 0, segment.size = 0.2, size = 2.5) +
geom_hline(yintercept = log10(1.5), lty = 2, size = 0.2) +
geom_hline(yintercept = log10(1/1.5), lty = 2, size = 0.2) +
geom_hline(yintercept = 0) +
scale_color_manual(values = c("#edae49", "#4988ed")) +
facet_wrap(~ unique_id,
scales = "free",
labeller = trajectory_labeller) +
labs(x = "Mutational Step",
y = expression(epsilon)) +
theme_classic() +
theme(text = element_text(size=9), axis.text = element_text(size=8, color = "black"),
legend.position = "none", axis.line=element_line()) +
scale_fill_manual(values = c("#4988ed", "#edae49")) +
scale_y_continuous(limits=c(-3,3))
## Warning: The labeller API has been updated. Labellers taking `variable` and
## `value` arguments are now deprecated. See labellers documentation.
adaptive_traj_plot
#ggsave("fig_4.svg", adaptive_traj_plot, width = 180, height = 247/2, dpi = 300, units = "mm")
Which one of the genotypes seen above are the outliers?
adaptive_traj %>%
filter(likely & mutations > 1) %>%
mutate(outlier = abs(means - avg) > log10(1.5)) %>%
select(c(genotype, unique_id)) %>%
knitr::kable()
| genotype | unique_id |
|---|---|
| x0xx00 | DHFR_ic75_palmer |
| 00xx00 | DHFR_ic75_palmer |
| xxxxx | DHFR_ki_trajg |
| 0xxxx | DHFR_ki_trajg |
| 0xx0x | DHFR_ki_trajg |
| 00x0x | DHFR_ki_trajg |
| xxxxx | DHFR_ki_trajr |
| xxx0x | DHFR_ki_trajr |
| 0xx0x | DHFR_ki_trajr |
| 00x0x | DHFR_ki_trajr |
| 0xx00 | MPH_catact_ZnPTM |
| 0xx0x | MPH_catact_ZnPTM |
| xxxxx | MPH_catact_ZnPTM |
| xxx0x | MPH_catact_ZnPTM |
| xxx00xx | NfsA_ec50_2039 |
| x0x00xx | NfsA_ec50_2039 |
| x0000x0 | NfsA_ec50_2039 |
| x0000xx | NfsA_ec50_2039 |
| xxxxxxx | NfsA_ec50_3637 |
| xxx0xxx | NfsA_ec50_3637 |
| x0x0xxx | NfsA_ec50_3637 |
| x0x00x0 | NfsA_ec50_3637 |
| x0x00xx | NfsA_ec50_3637 |
| x0000x0 | NfsA_ec50_3637 |
| xxxx | OXA-48_ic50_CAZtraj1 |
| 0xxx | OXA-48_ic50_CAZtraj1 |
| 0x0x | OXA-48_ic50_CAZtraj1 |
| 00x0x0 | OXA-48_ic50_CAZtraj2 |
| 00x0xx | OXA-48_ic50_CAZtraj2 |
| xxxxxx | OXA-48_ic50_CAZtraj2 |
| 0xxxxx | OXA-48_ic50_CAZtraj2 |
| 0xx0xx | OXA-48_ic50_CAZtraj2 |
| xx0x00 | OXA-48_ic50_CAZtraj3 |
| xx0x0x | OXA-48_ic50_CAZtraj3 |
| x00x00 | OXA-48_ic50_CAZtraj3 |
| xxx0x0 | PTE_catact_2NH |
| xx00x0 | PTE_catact_2NH |
| 0x00x0 | PTE_catact_2NH |
| 00x0x | TEM_MIC_weinreich |
| 0xxxx | TEM_MIC_weinreich |
| 0xx0x | TEM_MIC_weinreich |
| xxxxx | TEM_MIC_weinreich |
Now we swich gears to look at the entire adaptive landscapes, not just the most accessible trajectory.
What % of adaptive landscape genotypes are outliers from the mean? What % of adaptive accessible trajectory genotypes are outliers from the mean? What % of adaptive less accessible trajectory genotypes are outliers from the mean?
adaptive_traj_final = adaptive_traj %>%
mutate(outlier = abs(means - avg) > log10(1.5))
length(adaptive_traj_final$outlier)
## [1] 645
sum(adaptive_traj_final$outlier)
## [1] 309
sum(adaptive_traj_final$outlier) / length(adaptive_traj_final$outlier)
## [1] 0.4790698
length(filter(adaptive_traj_final, likely) %>% pull(outlier))
## [1] 53
sum(filter(adaptive_traj_final, likely) %>% pull(outlier))
## [1] 30
sum(filter(adaptive_traj_final, likely) %>% pull(outlier)) / length(filter(adaptive_traj_final, likely) %>% pull(outlier))
## [1] 0.5660377
length(filter(adaptive_traj_final, !likely) %>% pull(outlier))
## [1] 592
sum(filter(adaptive_traj_final, !likely) %>% pull(outlier))
## [1] 279
sum(filter(adaptive_traj_final, !likely) %>% pull(outlier)) / length(filter(adaptive_traj_final, !likely) %>% pull(outlier))
## [1] 0.4712838
Next we try to see if adaptive vs random trajectories show diminishing returns.
Doing for single mutants first for every single landscape…
higher_df_diminish = higher_df %>%
unite("partial_id", c(Enzyme, Measurement, Condition)) %>%
filter(mutations == 1) %>%
mutate(from = str_replace_all(genotype, "x", "0"),
to = str_replace_all(genotype, "x", "1"))
start_effect = c()
for(i in 1:dim(higher_df_diminish)[1]) {
if(!str_detect(higher_df_diminish$from[i], "1")) {
start_effect = c(start_effect, 0) # If starts from WT, start effect is 0
} else {
start_effect = c(start_effect, filter(fit_land, unique_id == higher_df_diminish$partial_id[i] & id == higher_df_diminish$from[i])
%>% pull(effect))
}
}
higher_df_diminish$start_effect = start_effect
ggplot(higher_df_diminish, aes(x = start_effect, y = avg)) +
geom_point() +
theme_classic()
Next only looking at adaptive landscapes
higher_df_diminish = higher_df %>%
unite("partial_id", c(Enzyme, Measurement, Condition)) %>%
filter(mutations == 1) %>%
mutate(from = str_replace_all(genotype, "x", "0"),
to = str_replace_all(genotype, "x", "1")) %>%
filter(partial_id %in% adaptive_trajectories)
start_effect = c()
for(i in 1:dim(higher_df_diminish)[1]) {
if(!str_detect(higher_df_diminish$from[i], "1")) {
start_effect = c(start_effect, 0) # If starts from WT, start effect is 0
} else {
start_effect = c(start_effect, filter(fit_land, unique_id == higher_df_diminish$partial_id[i] & id == higher_df_diminish$from[i])
%>% pull(effect))
}
}
higher_df_diminish$start_effect = start_effect
ggplot(higher_df_diminish, aes(x = start_effect, y = avg)) +
geom_point() +
facet_wrap(~ partial_id) +
geom_smooth(method = 'lm', formula = y ~ x) +
stat_cor(aes(label = paste(..rr.label.., ..p.label.., sep = "~~")), color = "red", geom = "label", label.x = -2.5, label.y = 4, r.accuracy = 0.01, p.accuracy = 0.01) +
theme_classic()
What about only looking at the best step from each starting genotype?
higher_df_diminish %>%
group_by(partial_id, from) %>%
summarise(avg = max(avg), start_effect = start_effect) %>%
distinct() %>%
ggplot(aes(x = start_effect, y = avg)) +
geom_point() +
geom_smooth(method = 'lm', formula = y ~ x) +
stat_cor(aes(label = paste(..rr.label.., ..p.label.., sep = "~~")), color = "red", geom = "label", label.x = -2, label.y = 4.7, r.accuracy = 0.01, p.accuracy = 0.01) +
facet_wrap(~ partial_id) +
theme_classic()
## `summarise()` has grouped output by 'partial_id', 'from'. You can override using the `.groups` argument.
What about only looking at the adaptive trajectories?
all_traj_points = all_traj %>%
filter(likely & unique_id %in% adaptive_trajectories) %>%
mutate(genotype = str_replace_all(genotype, "x", "1")) %>%
select(genotype, unique_id)
higher_df_diminish_traj = tibble()
prev_geno = c()
for(i in 1:dim(all_traj_points)[1]) {
if(length(prev_geno) > 0) {
higher_df_diminish_traj = rbind(higher_df_diminish_traj, higher_df_diminish %>% filter(partial_id == all_traj_points$unique_id[i] & to == all_traj_points$genotype[i]) %>% filter(from == prev_geno))
} else {
higher_df_diminish_traj = rbind(higher_df_diminish_traj, higher_df_diminish %>% filter(partial_id == all_traj_points$unique_id[i] & to == all_traj_points$genotype[i]))
}
if(i != dim(all_traj_points)[1]) {
if(all_traj_points$unique_id[i + 1] == all_traj_points$unique_id[i]) {
prev_geno = tail(higher_df_diminish_traj$to, 1)
} else {
prev_geno = c()
}
}
}
higher_df_diminish_traj %>%
ggplot(aes(x = start_effect, y = avg)) +
geom_point() +
geom_smooth(method = 'lm', formula = y ~ x) +
stat_cor(aes(label = paste(..rr.label.., ..p.label.., sep = "~~")), color = "red", geom = "label", label.x = 0, label.y = 3.7, r.accuracy = 0.01, p.accuracy = 0.01) +
facet_wrap(~ partial_id) +
theme_classic()
We can also do a side by side correlation comparison of starting effect vs MAXIMUM delFs as well as vs ALL POSSIBLE delFs
max_list = higher_df_diminish %>%
group_by(partial_id, from) %>%
summarise(avg = max(avg), start_effect = start_effect) %>%
distinct()
## `summarise()` has grouped output by 'partial_id', 'from'. You can override using the `.groups` argument.
maxes = c()
for(entry in 1:dim(higher_df_diminish)[1]) {
maxes = c(maxes, ifelse(any(higher_df_diminish[entry,]$from == max_list$from & higher_df_diminish[entry,]$partial_id == max_list$partial_id & higher_df_diminish[entry,]$avg == max_list$avg & higher_df_diminish[entry,]$start_effect == max_list$start_effect), T, F))
}
higher_df_diminish$is_max = maxes
higher_df_diminish_plot = higher_df_diminish %>%
ggplot(aes(x = start_effect, y = avg)) +
geom_point(aes(fill = is_max), shape = 21, color = "black", stroke = 0.1) +
geom_smooth(method = 'lm', formula = y ~ x, color = "grey") +
geom_smooth(data = higher_df_diminish %>% filter(is_max), method = 'lm', formula = y ~ x, color = "#edae49") +
stat_cor(aes(label = paste(..rr.label.., sep = "~~")), label.x = 2, label.y = 5.5, r.accuracy = 0.01, size = 2.5, color = "grey") +
stat_cor(data = higher_df_diminish %>% filter(is_max), aes(label = paste(..rr.label.., sep = "~~")), label.x = 2, label.y = 4, r.accuracy = 0.01, size = 2.5, color = "#edae49") +
facet_wrap(~ partial_id, scales = "free") +
labs(x = expression("Starting Function ("*italic("F")*")"),
y = expression(Delta*'F')) +
theme_classic() +
theme(text = element_text(size=9), axis.text = element_text(size=8, color = "black"),
legend.position = "none", axis.line=element_line()) +
scale_fill_manual(values = c("grey", "#edae49")) +
scale_x_continuous(limits=c(-6,6)) +
scale_y_continuous(limits=c(-6,6))
higher_df_diminish_plot
#ggsave("supp_fig_3.svg", higher_df_diminish_plot, width = 180, height = 247/2, dpi = 300, units = "mm")
Modify adaptive traj to also have the genotypic effect from fit_land. To do this I need to take the unique_id from adaptive traj and the genotype after x -> 1 modification from adaptive traj, and find the corresponding value of the genotype in fit_land
traj_effects = c()
for(i in 1:dim(adaptive_traj)[1]){
traj_effects= c(traj_effects, fit_land %>%
filter(unique_id == adaptive_traj$unique_id[i]) %>%
filter(id == str_replace_all(adaptive_traj$genotype[i], "x", "1")) %>%
pull(effect))
}
adaptive_traj$traj_effects = traj_effects
Now that adaptive_traj has genotype effects, we can ask the question: Did the idiosyncratic epistasis at that order specifically enable or restrict that genotype.
access = c()
access_no_idio = c()
access_mean = c()
for(i in 1:dim(adaptive_traj)[1]) {
current_test = adaptive_traj[i,]
if(current_test$mutations != 1) {
loc = str_locate_all(current_test$genotype, "x")[[1]][,1]
len = str_length(current_test$genotype)
func = c()
# Get all previous constituents
for(j in 1:length(loc)) {
const = current_test$genotype
substr(const, loc[j], loc[j]) = "0"
func = c(func, adaptive_traj %>% filter(unique_id == current_test$unique_id & genotype == const) %>% pull(traj_effects))
}
access = c(access, sum(current_test$traj_effects - func >= 0))
access_no_idio = c(access_no_idio, sum((current_test$traj_effects - current_test$avg) - func >= 0)) # Access without epistasis
access_mean = c(access_mean, sum((current_test$traj_effects - (current_test$avg - current_test$means)) - func >= 0))
} else {
access = c(access, NA)
access_no_idio = c(access_no_idio, NA)
access_mean = c(access_mean, NA)
}
}
Now let’s look at the accessibility metrics in a tibble
access_df = tibble(access = access,
access_no_idio = access_no_idio,
access_mean = access_mean)
access_df = access_df %>% mutate(access_w_mean = access_mean - access,
access_w_idio = access - access_no_idio)
adaptive_traj_access = cbind(adaptive_traj, access_df)
adaptive_traj_access
## genotype avg pos mutations likely
## 1 x00000 0.6434526765 p1 1 FALSE
## 2 xx0000 0.0120089075 p1|p21 2 FALSE
## 3 xxx000 4.0915005511 p1|p21|p26 3 FALSE
## 4 xxxx00 -3.4400739596 p1|p21|p26|p28 4 FALSE
## 5 xxxxx0 0.2127319126 p1|p21|p26|p28|p30 5 FALSE
## 6 xxxxxx -7.1891355892 p1|p21|p26|p28|p30|p94 6 FALSE
## 7 xxxx0x 10.4358637805 p1|p21|p26|p28|p94 5 FALSE
## 8 xxx0x0 -3.8204466134 p1|p21|p26|p30 4 FALSE
## 9 xxx0xx 2.5755327809 p1|p21|p26|p30|p94 5 FALSE
## 10 xxx00x -5.3028285005 p1|p21|p26|p94 4 FALSE
## 11 xx0x00 0.7938783708 p1|p21|p28 3 FALSE
## 12 xx0xx0 1.9958051082 p1|p21|p28|p30 4 FALSE
## 13 xx0xxx 3.1289862437 p1|p21|p28|p30|p94 5 FALSE
## 14 xx0x0x -5.2140863284 p1|p21|p28|p94 4 FALSE
## 15 xx00x0 -0.3089210603 p1|p21|p30 3 FALSE
## 16 xx00xx 1.3698610227 p1|p21|p30|p94 4 FALSE
## 17 xx000x 1.0635937667 p1|p21|p94 3 FALSE
## 18 x0x000 0.1951793213 p1|p26 2 FALSE
## 19 x0xx00 -0.1243552470 p1|p26|p28 3 TRUE
## 20 x0xxx0 0.2566376927 p1|p26|p28|p30 4 FALSE
## 21 x0xxxx 0.5135760858 p1|p26|p28|p30|p94 5 FALSE
## 22 x0xx0x -1.1042304716 p1|p26|p28|p94 4 FALSE
## 23 x0x0x0 -0.5804441706 p1|p26|p30 3 FALSE
## 24 x0x0xx 0.2071172162 p1|p26|p30|p94 4 FALSE
## 25 x0x00x 0.7200699261 p1|p26|p94 3 FALSE
## 26 x00x00 -0.5649786683 p1|p28 2 FALSE
## 27 x00xx0 -0.3853196942 p1|p28|p30 3 FALSE
## 28 x00xxx 2.2307063095 p1|p28|p30|p94 4 FALSE
## 29 x00x0x 1.0706822252 p1|p28|p94 3 FALSE
## 30 x000x0 0.8028661382 p1|p30 2 FALSE
## 31 x000xx -0.7695624415 p1|p30|p94 3 FALSE
## 32 x0000x -0.5587019239 p1|p94 2 FALSE
## 33 0x0000 0.7283537820 p21 1 FALSE
## 34 0xx000 -4.0673668714 p21|p26 2 FALSE
## 35 0xxx00 3.2599112109 p21|p26|p28 3 FALSE
## 36 0xxxx0 -0.1092526217 p21|p26|p28|p30 4 FALSE
## 37 0xxxxx 0.9545832341 p21|p26|p28|p30|p94 5 FALSE
## 38 0xxx0x -4.7257531982 p21|p26|p28|p94 4 FALSE
## 39 0xx0x0 3.7900356730 p21|p26|p30 3 FALSE
## 40 0xx0xx -1.8299171248 p21|p26|p30|p94 4 FALSE
## 41 0xx00x 5.0316956326 p21|p26|p94 3 FALSE
## 42 0x0x00 -1.3963522169 p21|p28 2 FALSE
## 43 0x0xx0 -1.6714200186 p21|p28|p30 3 FALSE
## 44 0x0xxx 2.6259368082 p21|p28|p30|p94 4 FALSE
## 45 0x0x0x 0.0760740241 p21|p28|p94 3 FALSE
## 46 0x00x0 -0.2888077281 p21|p30 2 FALSE
## 47 0x00xx -1.5696223558 p21|p30|p94 3 FALSE
## 48 0x000x -1.3782885860 p21|p94 2 FALSE
## 49 00x000 0.3074960379 p26 1 FALSE
## 50 00xx00 0.0301094048 p26|p28 2 TRUE
## 51 00xxx0 -0.5331881295 p26|p28|p30 3 FALSE
## 52 00xxxx 0.0894805599 p26|p28|p30|p94 4 FALSE
## 53 00xx0x 0.7498001253 p26|p28|p94 3 FALSE
## 54 00x0x0 0.4506380989 p26|p30 2 FALSE
## 55 00x0xx -0.4343233914 p26|p30|p94 3 FALSE
## 56 00x00x -0.7310344646 p26|p94 2 FALSE
## 57 000x00 1.8555191557 p28 1 TRUE
## 58 000xx0 -0.2985650502 p28|p30 2 FALSE
## 59 000xxx -2.0436471012 p28|p30|p94 3 FALSE
## 60 000x0x -1.6354286857 p28|p94 2 FALSE
## 61 0000x0 0.2671717284 p30 1 FALSE
## 62 0000xx 0.2248027433 p30|p94 2 FALSE
## 63 00000x 1.4727564493 p94 1 FALSE
## 64 x0000 0.3521825181 p21 1 FALSE
## 65 xx000 0.3307006663 p21|p26 2 FALSE
## 66 xxx00 0.3536996911 p21|p26|p28 3 FALSE
## 67 xxxx0 -0.1686927405 p21|p26|p28|p30 4 FALSE
## 68 xxxxx -0.4024022633 p21|p26|p28|p30|p94 5 TRUE
## 69 xxx0x -0.1734921898 p21|p26|p28|p94 4 FALSE
## 70 xx0x0 -0.1865486214 p21|p26|p30 3 FALSE
## 71 xx0xx 0.5272617148 p21|p26|p30|p94 4 FALSE
## 72 xx00x 0.0013352771 p21|p26|p94 3 FALSE
## 73 x0x00 0.2181874305 p21|p28 2 FALSE
## 74 x0xx0 -0.3131605459 p21|p28|p30 3 FALSE
## 75 x0xxx 1.8737920496 p21|p28|p30|p94 4 FALSE
## 76 x0x0x -0.9875093245 p21|p28|p94 3 FALSE
## 77 x00x0 0.7857643468 p21|p30 2 FALSE
## 78 x00xx -1.6593302600 p21|p30|p94 3 FALSE
## 79 x000x 0.8318208593 p21|p94 2 FALSE
## 80 0x000 0.3424226808 p26 1 FALSE
## 81 0xx00 0.3552586149 p26|p28 2 FALSE
## 82 0xxx0 -0.1441698207 p26|p28|p30 3 FALSE
## 83 0xxxx 0.3612176043 p26|p28|p30|p94 4 TRUE
## 84 0xx0x -0.0241508318 p26|p28|p94 3 TRUE
## 85 0x0x0 0.6355068000 p26|p30 2 FALSE
## 86 0x0xx -0.3616741906 p26|p30|p94 3 FALSE
## 87 0x00x 0.3207424929 p26|p94 2 FALSE
## 88 00x00 1.1303337685 p28 1 TRUE
## 89 00xx0 0.3513283213 p28|p30 2 FALSE
## 90 00xxx -1.7328982941 p28|p30|p94 3 FALSE
## 91 00x0x 0.6093656589 p28|p94 2 TRUE
## 92 000x0 0.3463529745 p30 1 FALSE
## 93 000xx 1.7722915291 p30|p94 2 FALSE
## 94 0000x 0.6201360550 p94 1 FALSE
## 95 x0000 0.3521825181 p21 1 FALSE
## 96 xx000 0.3307006663 p21|p26 2 FALSE
## 97 xxx00 0.3536996911 p21|p26|p28 3 FALSE
## 98 xxxx0 0.6065198689 p21|p26|p28|p30 4 FALSE
## 99 xxxxx -0.4985709562 p21|p26|p28|p30|p94 5 TRUE
## 100 xxx0x -0.1734921898 p21|p26|p28|p94 4 TRUE
## 101 xx0x0 -0.8297443935 p21|p26|p30 3 FALSE
## 102 xx0xx 0.3995443642 p21|p26|p30|p94 4 FALSE
## 103 xx00x 0.0013352771 p21|p26|p94 3 FALSE
## 104 x0x00 0.2181874305 p21|p28 2 FALSE
## 105 x0xx0 -0.8214691113 p21|p28|p30 3 FALSE
## 106 x0xxx 1.2697737173 p21|p28|p30|p94 4 FALSE
## 107 x0x0x -0.9875093245 p21|p28|p94 3 FALSE
## 108 x00x0 0.9314346760 p21|p30 2 FALSE
## 109 x00xx -0.7407359065 p21|p30|p94 3 FALSE
## 110 x000x 0.8318208593 p21|p94 2 FALSE
## 111 0x000 0.3424226808 p26 1 FALSE
## 112 0xx00 0.3552586149 p26|p28 2 FALSE
## 113 0xxx0 -0.7284481698 p26|p28|p30 3 FALSE
## 114 0xxxx -0.4379790036 p26|p28|p30|p94 4 FALSE
## 115 0xx0x -0.0241508318 p26|p28|p94 3 TRUE
## 116 0x0x0 0.5505948678 p26|p30 2 FALSE
## 117 0x0xx 0.5514262317 p26|p30|p94 3 FALSE
## 118 0x00x 0.3207424929 p26|p94 2 FALSE
## 119 00x00 1.1303337685 p28 1 TRUE
## 120 00xx0 0.8108607448 p28|p30 2 FALSE
## 121 00xxx -0.5692022823 p28|p30|p94 3 FALSE
## 122 00x0x 0.6093656589 p28|p94 2 TRUE
## 123 000x0 0.3598354823 p30 1 FALSE
## 124 000xx 0.0955754241 p30|p94 2 FALSE
## 125 0000x 0.6201360550 p94 1 FALSE
## 126 0x000 0.2718416065 p193 1 FALSE
## 127 0xx00 -0.0593328832 p193|p258 2 TRUE
## 128 0xxx0 -0.1290432971 p193|p258|p271 3 FALSE
## 129 0xxxx 0.3792062705 p193|p258|p271|p273 4 FALSE
## 130 0xx0x 0.0001438909 p193|p258|p273 3 TRUE
## 131 0x0x0 0.1201968421 p193|p271 2 FALSE
## 132 0x0xx -0.3739680940 p193|p271|p273 3 FALSE
## 133 0x00x 0.0402303690 p193|p273 2 FALSE
## 134 00x00 0.9943171527 p258 1 TRUE
## 135 00xx0 0.3449452409 p258|p271 2 FALSE
## 136 00xxx 0.3467543506 p258|p271|p273 3 FALSE
## 137 00x0x -0.6750368044 p258|p273 2 FALSE
## 138 000x0 -0.7878123956 p271 1 FALSE
## 139 000xx -0.0336181961 p271|p273 2 FALSE
## 140 0000x 0.6683859167 p273 1 FALSE
## 141 x0000 0.9309490312 p72 1 FALSE
## 142 xx000 -0.0444281456 p72|p193 2 FALSE
## 143 xxx00 -0.0616252468 p72|p193|p258 3 FALSE
## 144 xxxx0 0.1757744188 p72|p193|p258|p271 4 FALSE
## 145 xxxxx -0.6987406300 p72|p193|p258|p271|p273 5 TRUE
## 146 xxx0x 0.2309903363 p72|p193|p258|p273 4 TRUE
## 147 xx0x0 0.1600950393 p72|p193|p271 3 FALSE
## 148 xx0xx 0.3313957319 p72|p193|p271|p273 4 FALSE
## 149 xx00x 1.2255130337 p72|p193|p273 3 FALSE
## 150 x0x00 -0.8248956387 p72|p258 2 FALSE
## 151 x0xx0 -0.0721629612 p72|p258|p271 3 FALSE
## 152 x0xxx 0.4273976788 p72|p258|p271|p273 4 FALSE
## 153 x0x0x 0.5030730716 p72|p258|p273 3 FALSE
## 154 x00x0 -0.4747507189 p72|p271 2 FALSE
## 155 x00xx 0.9335230546 p72|p271|p273 3 FALSE
## 156 x000x -0.8883718289 p72|p273 2 FALSE
## 157 0x00000 -0.0319842860 p215 1 FALSE
## 158 0xx0000 -0.1001628233 p215|p219 2 FALSE
## 159 0xxx000 0.0153787276 p215|p219|p222 3 FALSE
## 160 0xxxx00 -0.0633206646 p215|p219|p222|p224 4 FALSE
## 161 0xxxxx0 0.1926165670 p215|p219|p222|p224|p225 5 FALSE
## 162 0xxxxxx -0.2219759139 p215|p219|p222|p224|p225|p227 6 FALSE
## 163 0xxxx0x 0.0329651279 p215|p219|p222|p224|p227 5 FALSE
## 164 0xxx0x0 -0.0715647374 p215|p219|p222|p225 4 FALSE
## 165 0xxx0xx 0.0125039835 p215|p219|p222|p225|p227 5 FALSE
## 166 0xxx00x -0.0171348145 p215|p219|p222|p227 4 FALSE
## 167 0xx0x00 0.2287508618 p215|p219|p224 3 FALSE
## 168 0xx0xx0 -0.5573811653 p215|p219|p224|p225 4 FALSE
## 169 0xx0xxx 0.5419563191 p215|p219|p224|p225|p227 5 FALSE
## 170 0xx0x0x -0.2168101698 p215|p219|p224|p227 4 FALSE
## 171 0xx00x0 0.4841288301 p215|p219|p225 3 FALSE
## 172 0xx00xx 0.0551844365 p215|p219|p225|p227 4 FALSE
## 173 0xx000x 0.0220534824 p215|p219|p227 3 FALSE
## 174 0x0x000 -0.0342948717 p215|p222 2 FALSE
## 175 0x0xx00 0.0091213703 p215|p222|p224 3 FALSE
## 176 0x0xxx0 -0.1289441345 p215|p222|p224|p225 4 FALSE
## 177 0x0xxxx 0.2850142144 p215|p222|p224|p225|p227 5 FALSE
## 178 0x0xx0x -0.0290875537 p215|p222|p224|p227 4 FALSE
## 179 0x0x0x0 0.0885173451 p215|p222|p225 3 FALSE
## 180 0x0x0xx -0.2909010728 p215|p222|p225|p227 4 FALSE
## 181 0x0x00x 0.0188444183 p215|p222|p227 3 FALSE
## 182 0x00x00 -0.2010871677 p215|p224 2 FALSE
## 183 0x00xx0 0.3700748331 p215|p224|p225 3 FALSE
## 184 0x00xxx -0.5139161127 p215|p224|p225|p227 4 FALSE
## 185 0x00x0x 0.1681704619 p215|p224|p227 3 FALSE
## 186 0x000x0 -0.8913090871 p215|p225 2 FALSE
## 187 0x000xx 0.3526147707 p215|p225|p227 3 FALSE
## 188 0x0000x 0.0260417374 p215|p227 2 FALSE
## 189 00x0000 -0.0123337351 p219 1 FALSE
## 190 00xx000 -0.0297276099 p219|p222 2 FALSE
## 191 00xxx00 0.1378040487 p219|p222|p224 3 FALSE
## 192 00xxxx0 -0.1976298326 p219|p222|p224|p225 4 FALSE
## 193 00xxxxx 0.2066059916 p219|p222|p224|p225|p227 5 FALSE
## 194 00xxx0x -0.1957233618 p219|p222|p224|p227 4 FALSE
## 195 00xx0x0 0.0822417628 p219|p222|p225 3 FALSE
## 196 00xx0xx 0.0219021491 p219|p222|p225|p227 4 FALSE
## 197 00xx00x 0.0425812929 p219|p222|p227 3 FALSE
## 198 00x0x00 -0.0057644870 p219|p224 2 FALSE
## 199 00x0xx0 0.0539641030 p219|p224|p225 3 FALSE
## 200 00x0xxx -0.0868396401 p219|p224|p225|p227 4 FALSE
## 201 00x0x0x 0.1130737103 p219|p224|p227 3 FALSE
## 202 00x00x0 0.0486117049 p219|p225 2 FALSE
## 203 00x00xx -0.0487624720 p219|p225|p227 3 FALSE
## 204 00x000x -0.0267534338 p219|p227 2 FALSE
## 205 000x000 -0.0109953843 p222 1 FALSE
## 206 000xx00 -0.0658875343 p222|p224 2 FALSE
## 207 000xxx0 0.0992125977 p222|p224|p225 3 FALSE
## 208 000xxxx -0.2254780967 p222|p224|p225|p227 4 FALSE
## 209 000xx0x 0.1095744206 p222|p224|p227 3 FALSE
## 210 000x0x0 -0.0761547914 p222|p225 2 FALSE
## 211 000x0xx 0.1197632640 p222|p225|p227 3 FALSE
## 212 000x00x -0.0040162881 p222|p227 2 FALSE
## 213 0000x00 -0.0087739243 p224 1 FALSE
## 214 0000xx0 -0.2006423982 p224|p225 2 FALSE
## 215 0000xxx 0.0878887543 p224|p225|p227 3 FALSE
## 216 0000x0x 0.0280149218 p224|p227 2 FALSE
## 217 00000x0 0.4216039269 p225 1 TRUE
## 218 00000xx 0.2144987325 p225|p227 2 FALSE
## 219 000000x -0.0540392964 p227 1 FALSE
## 220 x000000 0.0569048513 p41 1 FALSE
## 221 xx00000 0.0506263961 p41|p215 2 FALSE
## 222 xxx0000 -0.0627231786 p41|p215|p219 3 FALSE
## 223 xxxx000 -0.0019460191 p41|p215|p219|p222 4 FALSE
## 224 xxxxx00 -0.1058612514 p41|p215|p219|p222|p224 5 FALSE
## 225 xxxxxx0 -0.0957262083 p41|p215|p219|p222|p224|p225 6 FALSE
## 226 xxxxxxx 0.2315052064 p41|p215|p219|p222|p224|p225|p227 7 FALSE
## 227 xxxxx0x 0.1907593798 p41|p215|p219|p222|p224|p227 6 FALSE
## 228 xxxx0x0 0.0214190847 p41|p215|p219|p222|p225 5 FALSE
## 229 xxxx0xx 0.0081593586 p41|p215|p219|p222|p225|p227 6 FALSE
## 230 xxxx00x 0.0196881922 p41|p215|p219|p222|p227 5 FALSE
## 231 xxx0x00 -0.1831429066 p41|p215|p219|p224 4 FALSE
## 232 xxx0xx0 0.5158591532 p41|p215|p219|p224|p225 5 FALSE
## 233 xxx0xxx -0.4866806483 p41|p215|p219|p224|p225|p227 6 FALSE
## 234 xxx0x0x 0.0518608150 p41|p215|p219|p224|p227 5 FALSE
## 235 xxx00x0 -0.0477063317 p41|p215|p219|p225 4 FALSE
## 236 xxx00xx -0.2305882837 p41|p215|p219|p225|p227 5 TRUE
## 237 xxx000x -0.0020672916 p41|p215|p219|p227 4 FALSE
## 238 xx0x000 0.1075734359 p41|p215|p222 3 FALSE
## 239 xx0xx00 -0.0350398194 p41|p215|p222|p224 4 FALSE
## 240 xx0xxx0 0.2094494585 p41|p215|p222|p224|p225 5 FALSE
## 241 xx0xxxx -0.3781967126 p41|p215|p222|p224|p225|p227 6 FALSE
## 242 xx0xx0x -0.0454264566 p41|p215|p222|p224|p227 5 FALSE
## 243 xx0x0x0 -0.1459200610 p41|p215|p222|p225 4 FALSE
## 244 xx0x0xx 0.3418075893 p41|p215|p222|p225|p227 5 FALSE
## 245 xx0x00x -0.0638318091 p41|p215|p222|p227 4 FALSE
## 246 xx00x00 0.2626517929 p41|p215|p224 3 FALSE
## 247 xx00xx0 -0.3919394092 p41|p215|p224|p225 4 FALSE
## 248 xx00xxx 0.4214522026 p41|p215|p224|p225|p227 5 FALSE
## 249 xx00x0x -0.0970817486 p41|p215|p224|p227 4 FALSE
## 250 xx000x0 0.6668734105 p41|p215|p225 3 FALSE
## 251 xx000xx -0.1214038212 p41|p215|p225|p227 4 FALSE
## 252 xx0000x -0.0338553482 p41|p215|p227 3 FALSE
## 253 x0x0000 0.0161267241 p41|p219 2 FALSE
## 254 x0xx000 0.0757320893 p41|p219|p222 3 FALSE
## 255 x0xxx00 -0.1277116490 p41|p219|p222|p224 4 FALSE
## 256 x0xxxx0 0.2249480585 p41|p219|p222|p224|p225 5 FALSE
## 257 x0xxxxx -0.1244215482 p41|p219|p222|p224|p225|p227 6 FALSE
## 258 x0xxx0x 0.0459332099 p41|p219|p222|p224|p227 5 FALSE
## 259 x0xx0x0 -0.1191339860 p41|p219|p222|p225 4 FALSE
## 260 x0xx0xx 0.0434715736 p41|p219|p222|p225|p227 5 FALSE
## 261 x0xx00x -0.1015771086 p41|p219|p222|p227 4 FALSE
## 262 x0x0x00 0.1951750662 p41|p219|p224 3 FALSE
## 263 x0x0xx0 -0.2755877482 p41|p219|p224|p225 4 FALSE
## 264 x0x0xxx 0.2083877522 p41|p219|p224|p225|p227 5 FALSE
## 265 x0x0x0x -0.0711284703 p41|p219|p224|p227 4 FALSE
## 266 x0x00x0 0.0648984777 p41|p219|p225 3 FALSE
## 267 x0x00xx 0.0747386150 p41|p219|p225|p227 4 TRUE
## 268 x0x000x -0.0127959714 p41|p219|p227 3 FALSE
## 269 x00x000 -0.0622831799 p41|p222 2 FALSE
## 270 x00xx00 0.1098623961 p41|p222|p224 3 FALSE
## 271 x00xxx0 -0.1208533594 p41|p222|p224|p225 4 FALSE
## 272 x00xxxx 0.2077500748 p41|p222|p224|p225|p227 5 FALSE
## 273 x00xx0x -0.0600361592 p41|p222|p224|p227 4 FALSE
## 274 x00x0x0 0.0950060893 p41|p222|p225 3 FALSE
## 275 x00x0xx -0.1767229759 p41|p222|p225|p227 4 FALSE
## 276 x00x00x 0.0829781764 p41|p222|p227 3 FALSE
## 277 x000x00 -0.1999418100 p41|p224 2 FALSE
## 278 x000xx0 0.3525576663 p41|p224|p225 3 FALSE
## 279 x000xxx -0.2266875196 p41|p224|p225|p227 4 FALSE
## 280 x000x0x 0.0549668103 p41|p224|p227 3 FALSE
## 281 x0000x0 0.1405845524 p41|p225 2 TRUE
## 282 x0000xx 0.0088692029 p41|p225|p227 3 TRUE
## 283 x00000x -0.0433471779 p41|p227 2 FALSE
## 284 0x00000 -0.0716041477 p215 1 FALSE
## 285 0xx0000 0.0702616599 p215|p219 2 FALSE
## 286 0xxx000 -0.1661178297 p215|p219|p222 3 FALSE
## 287 0xxxx00 0.1151274674 p215|p219|p222|p224 4 FALSE
## 288 0xxxxx0 0.1702958534 p215|p219|p222|p224|p225 5 FALSE
## 289 0xxxxxx -0.0383917843 p215|p219|p222|p224|p225|p227 6 FALSE
## 290 0xxxx0x -0.2015535789 p215|p219|p222|p224|p227 5 FALSE
## 291 0xxx0x0 0.0884210889 p215|p219|p222|p225 4 FALSE
## 292 0xxx0xx -0.2920677752 p215|p219|p222|p225|p227 5 FALSE
## 293 0xxx00x 0.3133912769 p215|p219|p222|p227 4 FALSE
## 294 0xx0x00 -0.1755967376 p215|p219|p224 3 FALSE
## 295 0xx0xx0 0.1635033214 p215|p219|p224|p225 4 FALSE
## 296 0xx0xxx -0.3684600468 p215|p219|p224|p225|p227 5 FALSE
## 297 0xx0x0x 0.2739510323 p215|p219|p224|p227 4 FALSE
## 298 0xx00x0 0.3031382139 p215|p219|p225 3 FALSE
## 299 0xx00xx 0.0744959069 p215|p219|p225|p227 4 FALSE
## 300 0xx000x -0.3069067427 p215|p219|p227 3 FALSE
## 301 0x0x000 -0.0237575767 p215|p222 2 FALSE
## 302 0x0xx00 -0.0526310162 p215|p222|p224 3 FALSE
## 303 0x0xxx0 -0.0352420992 p215|p222|p224|p225 4 FALSE
## 304 0x0xxxx -0.4596032911 p215|p222|p224|p225|p227 5 FALSE
## 305 0x0xx0x 0.2141674822 p215|p222|p224|p227 4 FALSE
## 306 0x0x0x0 -0.0180887364 p215|p222|p225 3 FALSE
## 307 0x0x0xx 0.1691245166 p215|p222|p225|p227 4 FALSE
## 308 0x0x00x -0.2397684279 p215|p222|p227 3 FALSE
## 309 0x00x00 0.1232399177 p215|p224 2 FALSE
## 310 0x00xx0 0.0682601497 p215|p224|p225 3 FALSE
## 311 0x00xxx 0.3657729600 p215|p224|p225|p227 4 FALSE
## 312 0x00x0x -0.2486813029 p215|p224|p227 3 FALSE
## 313 0x000x0 -0.7363049373 p215|p225 2 FALSE
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## 641 xxxxx 0.2694733831 p4205|p42|p104|p182|p238 5 TRUE
## 642 xxx0x -1.3248172168 p4205|p42|p104|p238 4 FALSE
## 643 xx0x0 -1.2923762701 p4205|p42|p182 3 FALSE
## 644 xx0xx -0.9533284549 p4205|p42|p182|p238 4 FALSE
## 645 xx00x 1.1950828007 p4205|p42|p238 3 FALSE
## unique_id enzyme_name measure_type cond means
## 1 DHFR_ic75_palmer DHFR ic75 palmer 1.2062354930
## 2 DHFR_ic75_palmer DHFR ic75 palmer 0.8239107693
## 3 DHFR_ic75_palmer DHFR ic75 palmer 0.2172161842
## 4 DHFR_ic75_palmer DHFR ic75 palmer 0.0869399897
## 5 DHFR_ic75_palmer DHFR ic75 palmer -3.3818358819
## 6 DHFR_ic75_palmer DHFR ic75 palmer -7.1891355892
## 7 DHFR_ic75_palmer DHFR ic75 palmer 6.8412959859
## 8 DHFR_ic75_palmer DHFR ic75 palmer -4.2235981639
## 9 DHFR_ic75_palmer DHFR ic75 palmer -1.0190350137
## 10 DHFR_ic75_palmer DHFR ic75 palmer -0.5944141171
## 11 DHFR_ic75_palmer DHFR ic75 palmer 0.0104543165
## 12 DHFR_ic75_palmer DHFR ic75 palmer 1.8693802891
## 13 DHFR_ic75_palmer DHFR ic75 palmer -0.4655815508
## 14 DHFR_ic75_palmer DHFR ic75 palmer -0.2289452136
## 15 DHFR_ic75_palmer DHFR ic75 palmer 0.0443594841
## 16 DHFR_ic75_palmer DHFR ic75 palmer 2.4248366377
## 17 DHFR_ic75_palmer DHFR ic75 palmer -0.3734793838
## 18 DHFR_ic75_palmer DHFR ic75 palmer 0.2155007877
## 19 DHFR_ic75_palmer DHFR ic75 palmer -0.3762876201
## 20 DHFR_ic75_palmer DHFR ic75 palmer -1.1774922053
## 21 DHFR_ic75_palmer DHFR ic75 palmer -3.0809917088
## 22 DHFR_ic75_palmer DHFR ic75 palmer 2.5732055642
## 23 DHFR_ic75_palmer DHFR ic75 palmer -2.3319717766
## 24 DHFR_ic75_palmer DHFR ic75 palmer -0.0456122478
## 25 DHFR_ic75_palmer DHFR ic75 palmer 0.1027002613
## 26 DHFR_ic75_palmer DHFR ic75 palmer 0.1307275253
## 27 DHFR_ic75_palmer DHFR ic75 palmer 1.9214364729
## 28 DHFR_ic75_palmer DHFR ic75 palmer 2.2547035770
## 29 DHFR_ic75_palmer DHFR ic75 palmer 1.6478415588
## 30 DHFR_ic75_palmer DHFR ic75 palmer 0.6951950425
## 31 DHFR_ic75_palmer DHFR ic75 palmer 1.7901616616
## 32 DHFR_ic75_palmer DHFR ic75 palmer 0.1627485133
## 33 DHFR_ic75_palmer DHFR ic75 palmer -0.6411418079
## 34 DHFR_ic75_palmer DHFR ic75 palmer 0.5351546470
## 35 DHFR_ic75_palmer DHFR ic75 palmer 1.1245241043
## 36 DHFR_ic75_palmer DHFR ic75 palmer -1.3228789457
## 37 DHFR_ic75_palmer DHFR ic75 palmer -2.6399845605
## 38 DHFR_ic75_palmer DHFR ic75 palmer -0.8278135882
## 39 DHFR_ic75_palmer DHFR ic75 palmer 0.9472975263
## 40 DHFR_ic75_palmer DHFR ic75 palmer -1.8621430146
## 41 DHFR_ic75_palmer DHFR ic75 palmer 1.6952992211
## 42 DHFR_ic75_palmer DHFR ic75 palmer -0.9917867991
## 43 DHFR_ic75_palmer DHFR ic75 palmer 0.7602580277
## 44 DHFR_ic75_palmer DHFR ic75 palmer 2.8704376498
## 45 DHFR_ic75_palmer DHFR ic75 palmer -0.8496609691
## 46 DHFR_ic75_palmer DHFR ic75 palmer 0.2988833330
## 47 DHFR_ic75_palmer DHFR ic75 palmer 0.2794516133
## 48 DHFR_ic75_palmer DHFR ic75 palmer -0.6590651019
## 49 DHFR_ic75_palmer DHFR ic75 palmer 0.4601334499
## 50 DHFR_ic75_palmer DHFR ic75 palmer 0.5131687873
## 51 DHFR_ic75_palmer DHFR ic75 palmer -0.8931744546
## 52 DHFR_ic75_palmer DHFR ic75 palmer -0.9737236775
## 53 DHFR_ic75_palmer DHFR ic75 palmer -0.0430876032
## 54 DHFR_ic75_palmer DHFR ic75 palmer 0.3528148943
## 55 DHFR_ic75_palmer DHFR ic75 palmer -1.0887019893
## 56 DHFR_ic75_palmer DHFR ic75 palmer 0.4966773128
## 57 DHFR_ic75_palmer DHFR ic75 palmer 0.0248600677
## 58 DHFR_ic75_palmer DHFR ic75 palmer -0.6911103475
## 59 DHFR_ic75_palmer DHFR ic75 palmer 0.6800591798
## 60 DHFR_ic75_palmer DHFR ic75 palmer -1.8036554355
## 61 DHFR_ic75_palmer DHFR ic75 palmer 0.1999543556
## 62 DHFR_ic75_palmer DHFR ic75 palmer -0.5632148850
## 63 DHFR_ic75_palmer DHFR ic75 palmer -0.1737197244
## 64 DHFR_ki_trajg DHFR ki trajg 0.9697491865
## 65 DHFR_ki_trajg DHFR ki trajg 0.4109127529
## 66 DHFR_ki_trajg DHFR ki trajg 0.0820066602
## 67 DHFR_ki_trajg DHFR ki trajg -0.3698938721
## 68 DHFR_ki_trajg DHFR ki trajg -0.4024022633
## 69 DHFR_ki_trajg DHFR ki trajg -0.3746933214
## 70 DHFR_ki_trajg DHFR ki trajg -0.1078647001
## 71 DHFR_ki_trajg DHFR ki trajg 0.3260605832
## 72 DHFR_ki_trajg DHFR ki trajg 0.0776194738
## 73 DHFR_ki_trajg DHFR ki trajg 0.0773038378
## 74 DHFR_ki_trajg DHFR ki trajg 0.4387885429
## 75 DHFR_ki_trajg DHFR ki trajg 1.6725909180
## 76 DHFR_ki_trajg DHFR ki trajg -0.2379599604
## 77 DHFR_ki_trajg DHFR ki trajg 0.2140346062
## 78 DHFR_ki_trajg DHFR ki trajg -0.5594039435
## 79 DHFR_ki_trajg DHFR ki trajg 0.0156588163
## 80 DHFR_ki_trajg DHFR ki trajg 1.1162865010
## 81 DHFR_ki_trajg DHFR ki trajg 0.4024060198
## 82 DHFR_ki_trajg DHFR ki trajg -0.1485079546
## 83 DHFR_ki_trajg DHFR ki trajg 0.1600164727
## 84 DHFR_ki_trajg DHFR ki trajg -0.0308886904
## 85 DHFR_ki_trajg DHFR ki trajg 0.4189568454
## 86 DHFR_ki_trajg DHFR ki trajg -0.0180350968
## 87 DHFR_ki_trajg DHFR ki trajg 0.2569441197
## 88 DHFR_ki_trajg DHFR ki trajg 1.3968094488
## 89 DHFR_ki_trajg DHFR ki trajg -0.2775070636
## 90 DHFR_ki_trajg DHFR ki trajg -0.7159940330
## 91 DHFR_ki_trajg DHFR ki trajg -0.2978344832
## 92 DHFR_ki_trajg DHFR ki trajg 1.3184002269
## 93 DHFR_ki_trajg DHFR ki trajg 0.5356077160
## 94 DHFR_ki_trajg DHFR ki trajg 1.4946366750
## 95 DHFR_ki_trajr DHFR ki trajr 0.9837809276
## 96 DHFR_ki_trajr DHFR ki trajr 0.2391675950
## 97 DHFR_ki_trajr DHFR ki trajr 0.4455707916
## 98 DHFR_ki_trajr DHFR ki trajr 0.3572343908
## 99 DHFR_ki_trajr DHFR ki trajr -0.4985709562
## 100 DHFR_ki_trajr DHFR ki trajr -0.4227776679
## 101 DHFR_ki_trajr DHFR ki trajr -0.4513550160
## 102 DHFR_ki_trajr DHFR ki trajr 0.1502588862
## 103 DHFR_ki_trajr DHFR ki trajr -0.0102813748
## 104 DHFR_ki_trajr DHFR ki trajr -0.1460729622
## 105 DHFR_ki_trajr DHFR ki trajr -0.0079650572
## 106 DHFR_ki_trajr DHFR ki trajr 1.0204882392
## 107 DHFR_ki_trajr DHFR ki trajr -0.5640112998
## 108 DHFR_ki_trajr DHFR ki trajr 0.2420980885
## 109 DHFR_ki_trajr DHFR ki trajr -0.0307196048
## 110 DHFR_ki_trajr DHFR ki trajr 0.2800009857
## 111 DHFR_ki_trajr DHFR ki trajr 0.9702638982
## 112 DHFR_ki_trajr DHFR ki trajr 0.0922497590
## 113 DHFR_ki_trajr DHFR ki trajr -0.7688204762
## 114 DHFR_ki_trajr DHFR ki trajr -0.6872644817
## 115 DHFR_ki_trajr DHFR ki trajr -0.4545291676
## 116 DHFR_ki_trajr DHFR ki trajr 0.1269116399
## 117 DHFR_ki_trajr DHFR ki trajr 0.4075661730
## 118 DHFR_ki_trajr DHFR ki trajr 0.4697447546
## 119 DHFR_ki_trajr DHFR ki trajr 1.5598421003
## 120 DHFR_ki_trajr DHFR ki trajr 0.0485582393
## 121 DHFR_ki_trajr DHFR ki trajr -0.2779476645
## 122 DHFR_ki_trajr DHFR ki trajr -0.0788112990
## 123 DHFR_ki_trajr DHFR ki trajr 0.9680966144
## 124 DHFR_ki_trajr DHFR ki trajr -0.0381671545
## 125 DHFR_ki_trajr DHFR ki trajr 1.2077492397
## 126 MPH_catact_ZnPTM MPH catact ZnPTM 0.6014530846
## 127 MPH_catact_ZnPTM MPH catact ZnPTM -0.0454450320
## 128 MPH_catact_ZnPTM MPH catact ZnPTM -0.0262381099
## 129 MPH_catact_ZnPTM MPH catact ZnPTM 0.0298359555
## 130 MPH_catact_ZnPTM MPH catact ZnPTM 0.1305570369
## 131 MPH_catact_ZnPTM MPH catact ZnPTM 0.0829901928
## 132 MPH_catact_ZnPTM MPH catact ZnPTM -0.1933522503
## 133 MPH_catact_ZnPTM MPH catact ZnPTM 0.6141302903
## 134 MPH_catact_ZnPTM MPH catact ZnPTM 0.6419418606
## 135 MPH_catact_ZnPTM MPH catact ZnPTM 0.5759713003
## 136 MPH_catact_ZnPTM MPH catact ZnPTM 0.5753711677
## 137 MPH_catact_ZnPTM MPH catact ZnPTM -0.0779951552
## 138 MPH_catact_ZnPTM MPH catact ZnPTM -0.4725758154
## 139 MPH_catact_ZnPTM MPH catact ZnPTM 0.6166938011
## 140 MPH_catact_ZnPTM MPH catact ZnPTM 0.6761999762
## 141 MPH_catact_ZnPTM MPH catact ZnPTM 0.5888533443
## 142 MPH_catact_ZnPTM MPH catact ZnPTM 0.7147608105
## 143 MPH_catact_ZnPTM MPH catact ZnPTM -0.0329280268
## 144 MPH_catact_ZnPTM MPH catact ZnPTM -0.1735958962
## 145 MPH_catact_ZnPTM MPH catact ZnPTM -0.6987406300
## 146 MPH_catact_ZnPTM MPH catact ZnPTM -0.1183799787
## 147 MPH_catact_ZnPTM MPH catact ZnPTM 0.2389949571
## 148 MPH_catact_ZnPTM MPH catact ZnPTM -0.0179745831
## 149 MPH_catact_ZnPTM MPH catact ZnPTM 1.3320209103
## 150 MPH_catact_ZnPTM MPH catact ZnPTM -0.5190551772
## 151 MPH_catact_ZnPTM MPH catact ZnPTM 0.0547379301
## 152 MPH_catact_ZnPTM MPH catact ZnPTM 0.0780273638
## 153 MPH_catact_ZnPTM MPH catact ZnPTM 0.6575819217
## 154 MPH_catact_ZnPTM MPH catact ZnPTM 0.1822762261
## 155 MPH_catact_ZnPTM MPH catact ZnPTM 1.1382346025
## 156 MPH_catact_ZnPTM MPH catact ZnPTM 0.6027861091
## 157 NfsA_ec50_2039 NfsA ec50 2039 -0.1836051261
## 158 NfsA_ec50_2039 NfsA ec50 2039 0.0681046216
## 159 NfsA_ec50_2039 NfsA ec50 2039 -0.0186503117
## 160 NfsA_ec50_2039 NfsA ec50 2039 -0.0062579777
## 161 NfsA_ec50_2039 NfsA ec50 2039 0.0916418075
## 162 NfsA_ec50_2039 NfsA ec50 2039 -0.1062233107
## 163 NfsA_ec50_2039 NfsA ec50 2039 0.0752331625
## 164 NfsA_ec50_2039 NfsA ec50 2039 -0.0067424598
## 165 NfsA_ec50_2039 NfsA ec50 2039 -0.0365279925
## 166 NfsA_ec50_2039 NfsA ec50 2039 0.0386176943
## 167 NfsA_ec50_2039 NfsA ec50 2039 -0.0364612571
## 168 NfsA_ec50_2039 NfsA ec50 2039 -0.1043226875
## 169 NfsA_ec50_2039 NfsA ec50 2039 0.2455043396
## 170 NfsA_ec50_2039 NfsA ec50 2039 -0.0039551835
## 171 NfsA_ec50_2039 NfsA ec50 2039 0.3517777861
## 172 NfsA_ec50_2039 NfsA ec50 2039 0.0709342958
## 173 NfsA_ec50_2039 NfsA ec50 2039 -0.0105120513
## 174 NfsA_ec50_2039 NfsA ec50 2039 -0.0298045492
## 175 NfsA_ec50_2039 NfsA ec50 2039 -0.0255586573
## 176 NfsA_ec50_2039 NfsA ec50 2039 0.0695594275
## 177 NfsA_ec50_2039 NfsA ec50 2039 0.0428042028
## 178 NfsA_ec50_2039 NfsA ec50 2039 0.0337737283
## 179 NfsA_ec50_2039 NfsA ec50 2039 -0.0359432925
## 180 NfsA_ec50_2039 NfsA ec50 2039 -0.0903033454
## 181 NfsA_ec50_2039 NfsA ec50 2039 -0.0556827046
## 182 NfsA_ec50_2039 NfsA ec50 2039 -0.0281368788
## 183 NfsA_ec50_2039 NfsA ec50 2039 -0.0177819590
## 184 NfsA_ec50_2039 NfsA ec50 2039 -0.1324799126
## 185 NfsA_ec50_2039 NfsA ec50 2039 -0.0358644364
## 186 NfsA_ec50_2039 NfsA ec50 2039 -0.2410580287
## 187 NfsA_ec50_2039 NfsA ec50 2039 0.1397653277
## 188 NfsA_ec50_2039 NfsA ec50 2039 0.0963359828
## 189 NfsA_ec50_2039 NfsA ec50 2039 0.1058026609
## 190 NfsA_ec50_2039 NfsA ec50 2039 0.0075018912
## 191 NfsA_ec50_2039 NfsA ec50 2039 -0.0220382404
## 192 NfsA_ec50_2039 NfsA ec50 2039 0.0328627091
## 193 NfsA_ec50_2039 NfsA ec50 2039 0.0912835621
## 194 NfsA_ec50_2039 NfsA ec50 2039 -0.0629425668
## 195 NfsA_ec50_2039 NfsA ec50 2039 0.0346434105
## 196 NfsA_ec50_2039 NfsA ec50 2039 0.0975715483
## 197 NfsA_ec50_2039 NfsA ec50 2039 -0.0173590203
## 198 NfsA_ec50_2039 NfsA ec50 2039 0.0653162028
## 199 NfsA_ec50_2039 NfsA ec50 2039 -0.1337930941
## 200 NfsA_ec50_2039 NfsA ec50 2039 0.2123040146
## 201 NfsA_ec50_2039 NfsA ec50 2039 0.0339294274
## 202 NfsA_ec50_2039 NfsA ec50 2039 0.2326842483
## 203 NfsA_ec50_2039 NfsA ec50 2039 0.0906688737
## 204 NfsA_ec50_2039 NfsA ec50 2039 -0.0193446645
## 205 NfsA_ec50_2039 NfsA ec50 2039 -0.0274577404
## 206 NfsA_ec50_2039 NfsA ec50 2039 -0.0044199260
## 207 NfsA_ec50_2039 NfsA ec50 2039 0.0062850048
## 208 NfsA_ec50_2039 NfsA ec50 2039 -0.0280033491
## 209 NfsA_ec50_2039 NfsA ec50 2039 -0.0146378985
## 210 NfsA_ec50_2039 NfsA ec50 2039 -0.0229229906
## 211 NfsA_ec50_2039 NfsA ec50 2039 -0.0166336540
## 212 NfsA_ec50_2039 NfsA ec50 2039 0.0163758077
## 213 NfsA_ec50_2039 NfsA ec50 2039 -0.0629292093
## 214 NfsA_ec50_2039 NfsA ec50 2039 -0.0978057282
## 215 NfsA_ec50_2039 NfsA ec50 2039 -0.1077205690
## 216 NfsA_ec50_2039 NfsA ec50 2039 0.0521695212
## 217 NfsA_ec50_2039 NfsA ec50 2039 0.4787066672
## 218 NfsA_ec50_2039 NfsA ec50 2039 0.2889894811
## 219 NfsA_ec50_2039 NfsA ec50 2039 0.1275384125
## 220 NfsA_ec50_2039 NfsA ec50 2039 0.3094435780
## 221 NfsA_ec50_2039 NfsA ec50 2039 0.3579534467
## 222 NfsA_ec50_2039 NfsA ec50 2039 -0.1455269649
## 223 NfsA_ec50_2039 NfsA ec50 2039 0.0204132770
## 224 NfsA_ec50_2039 NfsA ec50 2039 -0.0004683640
## 225 NfsA_ec50_2039 NfsA ec50 2039 0.0200263949
## 226 NfsA_ec50_2039 NfsA ec50 2039 0.2315052064
## 227 NfsA_ec50_2039 NfsA ec50 2039 0.3065119830
## 228 NfsA_ec50_2039 NfsA ec50 2039 0.0355119615
## 229 NfsA_ec50_2039 NfsA ec50 2039 0.1239119618
## 230 NfsA_ec50_2039 NfsA ec50 2039 0.1770238631
## 231 NfsA_ec50_2039 NfsA ec50 2039 -0.0211872665
## 232 NfsA_ec50_2039 NfsA ec50 2039 0.2825320265
## 233 NfsA_ec50_2039 NfsA ec50 2039 -0.3709280451
## 234 NfsA_ec50_2039 NfsA ec50 2039 -0.0382235176
## 235 NfsA_ec50_2039 NfsA ec50 2039 -0.0089850783
## 236 NfsA_ec50_2039 NfsA ec50 2039 -0.4119726269
## 237 NfsA_ec50_2039 NfsA ec50 2039 -0.1245892565
## 238 NfsA_ec50_2039 NfsA ec50 2039 0.0745672884
## 239 NfsA_ec50_2039 NfsA ec50 2039 -0.0478116786
## 240 NfsA_ec50_2039 NfsA ec50 2039 0.0303642996
## 241 NfsA_ec50_2039 NfsA ec50 2039 -0.2624441094
## 242 NfsA_ec50_2039 NfsA ec50 2039 -0.0812688215
## 243 NfsA_ec50_2039 NfsA ec50 2039 0.0529152655
## 244 NfsA_ec50_2039 NfsA ec50 2039 0.2146652139
## 245 NfsA_ec50_2039 NfsA ec50 2039 0.0783215107
## 246 NfsA_ec50_2039 NfsA ec50 2039 0.0891218831
## 247 NfsA_ec50_2039 NfsA ec50 2039 -0.0297717435
## 248 NfsA_ec50_2039 NfsA ec50 2039 0.0468898238
## 249 NfsA_ec50_2039 NfsA ec50 2039 -0.0227298126
## 250 NfsA_ec50_2039 NfsA ec50 2039 0.5286519492
## 251 NfsA_ec50_2039 NfsA ec50 2039 -0.0403094169
## 252 NfsA_ec50_2039 NfsA ec50 2039 -0.1051249211
## 253 NfsA_ec50_2039 NfsA ec50 2039 0.0073342133
## 254 NfsA_ec50_2039 NfsA ec50 2039 -0.0252371270
## 255 NfsA_ec50_2039 NfsA ec50 2039 -0.0236105839
## 256 NfsA_ec50_2039 NfsA ec50 2039 0.1727504818
## 257 NfsA_ec50_2039 NfsA ec50 2039 -0.0086689450
## 258 NfsA_ec50_2039 NfsA ec50 2039 0.1369784273
## 259 NfsA_ec50_2039 NfsA ec50 2039 0.0017264238
## 260 NfsA_ec50_2039 NfsA ec50 2039 0.0432167804
## 261 NfsA_ec50_2039 NfsA ec50 2039 0.0005318277
## 262 NfsA_ec50_2039 NfsA ec50 2039 0.0516320608
## 263 NfsA_ec50_2039 NfsA ec50 2039 0.0512407834
## 264 NfsA_ec50_2039 NfsA ec50 2039 -0.0392870444
## 265 NfsA_ec50_2039 NfsA ec50 2039 0.0058148649
## 266 NfsA_ec50_2039 NfsA ec50 2039 0.0040635316
## 267 NfsA_ec50_2039 NfsA ec50 2039 -0.0364234226
## 268 NfsA_ec50_2039 NfsA ec50 2039 -0.0651786412
## 269 NfsA_ec50_2039 NfsA ec50 2039 0.0377675481
## 270 NfsA_ec50_2039 NfsA ec50 2039 0.0357611152
## 271 NfsA_ec50_2039 NfsA ec50 2039 0.0795724700
## 272 NfsA_ec50_2039 NfsA ec50 2039 0.0143172459
## 273 NfsA_ec50_2039 NfsA ec50 2039 -0.0049343146
## 274 NfsA_ec50_2039 NfsA ec50 2039 0.0165982946
## 275 NfsA_ec50_2039 NfsA ec50 2039 0.0251150682
## 276 NfsA_ec50_2039 NfsA ec50 2039 0.0117068309
## 277 NfsA_ec50_2039 NfsA ec50 2039 0.0584999147
## 278 NfsA_ec50_2039 NfsA ec50 2039 0.1708262589
## 279 NfsA_ec50_2039 NfsA ec50 2039 -0.0262790812
## 280 NfsA_ec50_2039 NfsA ec50 2039 -0.0353591048
## 281 NfsA_ec50_2039 NfsA ec50 2039 0.5323078372
## 282 NfsA_ec50_2039 NfsA ec50 2039 -0.0762717891
## 283 NfsA_ec50_2039 NfsA ec50 2039 -0.1138374545
## 284 NfsA_ec50_3637 NfsA ec50 3637 -0.0569706652
## 285 NfsA_ec50_3637 NfsA ec50 3637 0.0207202924
## 286 NfsA_ec50_3637 NfsA ec50 3637 0.0617297495
## 287 NfsA_ec50_3637 NfsA ec50 3637 -0.1321747554
## 288 NfsA_ec50_3637 NfsA ec50 3637 0.2251203280
## 289 NfsA_ec50_3637 NfsA ec50 3637 0.9181359551
## 290 NfsA_ec50_3637 NfsA ec50 3637 -0.2032811849
## 291 NfsA_ec50_3637 NfsA ec50 3637 -0.0733541727
## 292 NfsA_ec50_3637 NfsA ec50 3637 -0.3954314003
## 293 NfsA_ec50_3637 NfsA ec50 3637 0.1603000840
## 294 NfsA_ec50_3637 NfsA ec50 3637 0.0692931523
## 295 NfsA_ec50_3637 NfsA ec50 3637 0.0172715963
## 296 NfsA_ec50_3637 NfsA ec50 3637 -0.2790421770
## 297 NfsA_ec50_3637 NfsA ec50 3637 0.0926146264
## 298 NfsA_ec50_3637 NfsA ec50 3637 0.2187172032
## 299 NfsA_ec50_3637 NfsA ec50 3637 -0.2894908905
## 300 NfsA_ec50_3637 NfsA ec50 3637 -0.1314996851
## 301 NfsA_ec50_3637 NfsA ec50 3637 -0.0734456760
## 302 NfsA_ec50_3637 NfsA ec50 3637 -0.0248115637
## 303 NfsA_ec50_3637 NfsA ec50 3637 -0.2871770486
## 304 NfsA_ec50_3637 NfsA ec50 3637 -0.1946197462
## 305 NfsA_ec50_3637 NfsA ec50 3637 0.0122059190
## 306 NfsA_ec50_3637 NfsA ec50 3637 0.0057623657
## 307 NfsA_ec50_3637 NfsA ec50 3637 0.0236221014
## 308 NfsA_ec50_3637 NfsA ec50 3637 -0.0741606229
## 309 NfsA_ec50_3637 NfsA ec50 3637 0.0225808245
## 310 NfsA_ec50_3637 NfsA ec50 3637 0.1432529308
## 311 NfsA_ec50_3637 NfsA ec50 3637 0.1044974531
## 312 NfsA_ec50_3637 NfsA ec50 3637 -0.1794361674
## 313 NfsA_ec50_3637 NfsA ec50 3637 -0.1712352281
## 314 NfsA_ec50_3637 NfsA ec50 3637 0.1681112032
## 315 NfsA_ec50_3637 NfsA ec50 3637 0.1876979101
## 316 NfsA_ec50_3637 NfsA ec50 3637 0.1242066126
## 317 NfsA_ec50_3637 NfsA ec50 3637 0.0060707290
## 318 NfsA_ec50_3637 NfsA ec50 3637 0.0273060835
## 319 NfsA_ec50_3637 NfsA ec50 3637 -0.0580679126
## 320 NfsA_ec50_3637 NfsA ec50 3637 -0.1621399356
## 321 NfsA_ec50_3637 NfsA ec50 3637 -0.0461866784
## 322 NfsA_ec50_3637 NfsA ec50 3637 0.0826201157
## 323 NfsA_ec50_3637 NfsA ec50 3637 0.0611866980
## 324 NfsA_ec50_3637 NfsA ec50 3637 -0.0234087043
## 325 NfsA_ec50_3637 NfsA ec50 3637 0.0748541626
## 326 NfsA_ec50_3637 NfsA ec50 3637 0.0553774954
## 327 NfsA_ec50_3637 NfsA ec50 3637 0.0922134343
## 328 NfsA_ec50_3637 NfsA ec50 3637 -0.0564313255
## 329 NfsA_ec50_3637 NfsA ec50 3637 0.0927015930
## 330 NfsA_ec50_3637 NfsA ec50 3637 -0.1914053813
## 331 NfsA_ec50_3637 NfsA ec50 3637 0.0034482452
## 332 NfsA_ec50_3637 NfsA ec50 3637 0.0743519850
## 333 NfsA_ec50_3637 NfsA ec50 3637 -0.0222180181
## 334 NfsA_ec50_3637 NfsA ec50 3637 0.1118174567
## 335 NfsA_ec50_3637 NfsA ec50 3637 0.1675711646
## 336 NfsA_ec50_3637 NfsA ec50 3637 -0.0544554698
## 337 NfsA_ec50_3637 NfsA ec50 3637 -0.0782406699
## 338 NfsA_ec50_3637 NfsA ec50 3637 -0.1210110456
## 339 NfsA_ec50_3637 NfsA ec50 3637 0.0633115530
## 340 NfsA_ec50_3637 NfsA ec50 3637 -0.0562297539
## 341 NfsA_ec50_3637 NfsA ec50 3637 0.3126121933
## 342 NfsA_ec50_3637 NfsA ec50 3637 -0.0750305493
## 343 NfsA_ec50_3637 NfsA ec50 3637 -0.0653991572
## 344 NfsA_ec50_3637 NfsA ec50 3637 0.6005381669
## 345 NfsA_ec50_3637 NfsA ec50 3637 0.0523536890
## 346 NfsA_ec50_3637 NfsA ec50 3637 0.1660586859
## 347 NfsA_ec50_3637 NfsA ec50 3637 0.2481057780
## 348 NfsA_ec50_3637 NfsA ec50 3637 0.2334684124
## 349 NfsA_ec50_3637 NfsA ec50 3637 -0.0902993257
## 350 NfsA_ec50_3637 NfsA ec50 3637 0.1100160218
## 351 NfsA_ec50_3637 NfsA ec50 3637 -0.4441508278
## 352 NfsA_ec50_3637 NfsA ec50 3637 0.1480407335
## 353 NfsA_ec50_3637 NfsA ec50 3637 1.9130554790
## 354 NfsA_ec50_3637 NfsA ec50 3637 0.0349365724
## 355 NfsA_ec50_3637 NfsA ec50 3637 -0.1825827093
## 356 NfsA_ec50_3637 NfsA ec50 3637 -0.1683354659
## 357 NfsA_ec50_3637 NfsA ec50 3637 0.2066348605
## 358 NfsA_ec50_3637 NfsA ec50 3637 0.1466547909
## 359 NfsA_ec50_3637 NfsA ec50 3637 -0.0751033646
## 360 NfsA_ec50_3637 NfsA ec50 3637 0.2172275239
## 361 NfsA_ec50_3637 NfsA ec50 3637 0.2265367060
## 362 NfsA_ec50_3637 NfsA ec50 3637 -0.2405484081
## 363 NfsA_ec50_3637 NfsA ec50 3637 -0.0482498806
## 364 NfsA_ec50_3637 NfsA ec50 3637 0.1296145456
## 365 NfsA_ec50_3637 NfsA ec50 3637 0.1452661161
## 366 NfsA_ec50_3637 NfsA ec50 3637 0.0166145089
## 367 NfsA_ec50_3637 NfsA ec50 3637 -0.1953665691
## 368 NfsA_ec50_3637 NfsA ec50 3637 0.5683588741
## 369 NfsA_ec50_3637 NfsA ec50 3637 0.2764296357
## 370 NfsA_ec50_3637 NfsA ec50 3637 0.1256842504
## 371 NfsA_ec50_3637 NfsA ec50 3637 0.4798621280
## 372 NfsA_ec50_3637 NfsA ec50 3637 0.1207426029
## 373 NfsA_ec50_3637 NfsA ec50 3637 -0.1216801227
## 374 NfsA_ec50_3637 NfsA ec50 3637 -0.0055669317
## 375 NfsA_ec50_3637 NfsA ec50 3637 0.3247082162
## 376 NfsA_ec50_3637 NfsA ec50 3637 -0.1909947989
## 377 NfsA_ec50_3637 NfsA ec50 3637 0.2095135629
## 378 NfsA_ec50_3637 NfsA ec50 3637 -0.4965798123
## 379 NfsA_ec50_3637 NfsA ec50 3637 -0.1818802618
## 380 NfsA_ec50_3637 NfsA ec50 3637 -0.0123674545
## 381 NfsA_ec50_3637 NfsA ec50 3637 -0.0199667765
## 382 NfsA_ec50_3637 NfsA ec50 3637 0.1093012756
## 383 NfsA_ec50_3637 NfsA ec50 3637 -0.1985136418
## 384 NfsA_ec50_3637 NfsA ec50 3637 -0.4374371314
## 385 NfsA_ec50_3637 NfsA ec50 3637 0.0334701705
## 386 NfsA_ec50_3637 NfsA ec50 3637 0.1032024493
## 387 NfsA_ec50_3637 NfsA ec50 3637 -0.1092382943
## 388 NfsA_ec50_3637 NfsA ec50 3637 -0.0813533852
## 389 NfsA_ec50_3637 NfsA ec50 3637 -0.1803026806
## 390 NfsA_ec50_3637 NfsA ec50 3637 -0.0089813811
## 391 NfsA_ec50_3637 NfsA ec50 3637 0.3256607652
## 392 NfsA_ec50_3637 NfsA ec50 3637 -0.0887530639
## 393 NfsA_ec50_3637 NfsA ec50 3637 -0.0427680980
## 394 NfsA_ec50_3637 NfsA ec50 3637 -0.1243121143
## 395 NfsA_ec50_3637 NfsA ec50 3637 0.2248910047
## 396 NfsA_ec50_3637 NfsA ec50 3637 0.0124182728
## 397 NfsA_ec50_3637 NfsA ec50 3637 -0.1179390815
## 398 NfsA_ec50_3637 NfsA ec50 3637 0.2463504336
## 399 NfsA_ec50_3637 NfsA ec50 3637 -0.0788818339
## 400 NfsA_ec50_3637 NfsA ec50 3637 -0.0385841910
## 401 NfsA_ec50_3637 NfsA ec50 3637 0.0355885181
## 402 NfsA_ec50_3637 NfsA ec50 3637 -0.2867550554
## 403 NfsA_ec50_3637 NfsA ec50 3637 -0.0555546182
## 404 NfsA_ec50_3637 NfsA ec50 3637 -0.0906614568
## 405 NfsA_ec50_3637 NfsA ec50 3637 0.0375301472
## 406 NfsA_ec50_3637 NfsA ec50 3637 0.0669668060
## 407 NfsA_ec50_3637 NfsA ec50 3637 0.1206408259
## 408 NfsA_ec50_3637 NfsA ec50 3637 0.2984792456
## 409 NfsA_ec50_3637 NfsA ec50 3637 -0.3608554723
## 410 NfsA_ec50_3637 NfsA ec50 3637 -0.0562624733
## 411 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.3320456373
## 412 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 -0.0018436095
## 413 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.3308587296
## 414 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.0257601887
## 415 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 -0.0309832222
## 416 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.1279913659
## 417 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 -0.0362998491
## 418 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.0051600705
## 419 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.2345885840
## 420 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.2724234489
## 421 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.0021184273
## 422 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.9278873633
## 423 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.4520073760
## 424 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 -0.4140018406
## 425 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.5298022887
## 426 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.5072447682
## 427 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.2322662635
## 428 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.2011161233
## 429 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.3675270797
## 430 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0776672023
## 431 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.2575459450
## 432 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1957446004
## 433 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1469379670
## 434 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1729186185
## 435 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1034867917
## 436 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0245774437
## 437 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0962601272
## 438 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.3783664636
## 439 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0374030371
## 440 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.3571733510
## 441 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1761771660
## 442 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.2310929181
## 443 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1550315534
## 444 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.0836030575
## 445 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.2079829987
## 446 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0636474609
## 447 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1537873915
## 448 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.3862382251
## 449 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1370188947
## 450 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1334705379
## 451 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0620513175
## 452 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0826698657
## 453 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0804127445
## 454 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1021430941
## 455 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0291861163
## 456 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1708379190
## 457 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1726222318
## 458 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0526166129
## 459 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.0839710533
## 460 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.0835174321
## 461 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.2371716121
## 462 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0177659322
## 463 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.0023086958
## 464 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1701052593
## 465 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.2339087945
## 466 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1575969765
## 467 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1286436265
## 468 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.2046346501
## 469 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1116113314
## 470 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.3103155767
## 471 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1005210644
## 472 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1105589638
## 473 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.2858996573
## 474 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.2687554330
## 475 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0179728892
## 476 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1203778663
## 477 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.0130191319
## 478 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.2929293612
## 479 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1523968960
## 480 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.2308520929
## 481 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1963371276
## 482 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1482655882
## 483 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1611956215
## 484 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1346938665
## 485 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.2240176489
## 486 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0151834977
## 487 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.0503255758
## 488 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1977983004
## 489 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0610598579
## 490 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0409902741
## 491 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0960047615
## 492 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0319766797
## 493 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.2597174797
## 494 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.1646899644
## 495 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0126269844
## 496 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0418167864
## 497 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.1029315531
## 498 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0897087574
## 499 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0399018082
## 500 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0179960429
## 501 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0975201602
## 502 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0398744580
## 503 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0192487103
## 504 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0011875442
## 505 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0520545989
## 506 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.1274031534
## 507 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0016661053
## 508 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0788291217
## 509 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.1658703587
## 510 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0128113183
## 511 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0273208218
## 512 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0032190631
## 513 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.7306148862
## 514 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0337077327
## 515 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0415368472
## 516 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.1253463593
## 517 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0134890705
## 518 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0119185296
## 519 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0273295870
## 520 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.3584109834
## 521 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0458425116
## 522 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.1030318338
## 523 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0849993482
## 524 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.2143528103
## 525 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.4726135136
## 526 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.2156369908
## 527 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.1649320251
## 528 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.1671852596
## 529 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0842277227
## 530 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.1740119280
## 531 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0864575198
## 532 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0255847522
## 533 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0514435847
## 534 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0684301233
## 535 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.2718614986
## 536 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0723157477
## 537 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0957845105
## 538 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.1378797455
## 539 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.1277320188
## 540 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.3382977306
## 541 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0046617010
## 542 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0642127088
## 543 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.1439535601
## 544 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0021469899
## 545 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.6629417155
## 546 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0106092530
## 547 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0314345394
## 548 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.1211614184
## 549 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0413610788
## 550 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0566767551
## 551 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0922228366
## 552 PTE_catact_2NH PTE catact 2NH 0.7285390388
## 553 PTE_catact_2NH PTE catact 2NH 0.6373364226
## 554 PTE_catact_2NH PTE catact 2NH -0.2450705571
## 555 PTE_catact_2NH PTE catact 2NH -0.4956750948
## 556 PTE_catact_2NH PTE catact 2NH -0.6668673267
## 557 PTE_catact_2NH PTE catact 2NH 0.2725240242
## 558 PTE_catact_2NH PTE catact 2NH -0.5812863605
## 559 PTE_catact_2NH PTE catact 2NH -0.3335239493
## 560 PTE_catact_2NH PTE catact 2NH -1.6213040114
## 561 PTE_catact_2NH PTE catact 2NH -0.2268754483
## 562 PTE_catact_2NH PTE catact 2NH -0.0235867814
## 563 PTE_catact_2NH PTE catact 2NH -0.2614621340
## 564 PTE_catact_2NH PTE catact 2NH 0.6199565695
## 565 PTE_catact_2NH PTE catact 2NH 0.3578151577
## 566 PTE_catact_2NH PTE catact 2NH -0.8021822297
## 567 PTE_catact_2NH PTE catact 2NH 0.0947106215
## 568 PTE_catact_2NH PTE catact 2NH -0.0564936082
## 569 PTE_catact_2NH PTE catact 2NH 0.0769863183
## 570 PTE_catact_2NH PTE catact 2NH 0.0904603121
## 571 PTE_catact_2NH PTE catact 2NH 0.2717675785
## 572 PTE_catact_2NH PTE catact 2NH 0.8347845561
## 573 PTE_catact_2NH PTE catact 2NH 0.3473048566
## 574 PTE_catact_2NH PTE catact 2NH -0.1365751694
## 575 PTE_catact_2NH PTE catact 2NH -0.5099230009
## 576 PTE_catact_2NH PTE catact 2NH 0.2087296979
## 577 PTE_catact_2NH PTE catact 2NH -0.4068882602
## 578 PTE_catact_2NH PTE catact 2NH -0.2946447369
## 579 PTE_catact_2NH PTE catact 2NH -0.2780698745
## 580 PTE_catact_2NH PTE catact 2NH -0.1593031117
## 581 PTE_catact_2NH PTE catact 2NH -0.5955769994
## 582 PTE_catact_2NH PTE catact 2NH -0.0596301158
## 583 PTE_catact_2NH PTE catact 2NH -0.1570006989
## 584 PTE_catact_2NH PTE catact 2NH 1.4715059467
## 585 PTE_catact_2NH PTE catact 2NH 0.0137585293
## 586 PTE_catact_2NH PTE catact 2NH -0.1103822900
## 587 PTE_catact_2NH PTE catact 2NH -0.3977623654
## 588 PTE_catact_2NH PTE catact 2NH -0.2347492538
## 589 PTE_catact_2NH PTE catact 2NH -0.2301507057
## 590 PTE_catact_2NH PTE catact 2NH -0.3752671809
## 591 PTE_catact_2NH PTE catact 2NH -1.1402236356
## 592 PTE_catact_2NH PTE catact 2NH -0.2300782640
## 593 PTE_catact_2NH PTE catact 2NH 0.3597276308
## 594 PTE_catact_2NH PTE catact 2NH 0.1752027516
## 595 PTE_catact_2NH PTE catact 2NH 0.0086221024
## 596 PTE_catact_2NH PTE catact 2NH 0.0744053780
## 597 PTE_catact_2NH PTE catact 2NH -0.3127144804
## 598 PTE_catact_2NH PTE catact 2NH -0.0655980266
## 599 PTE_catact_2NH PTE catact 2NH 0.1022297216
## 600 PTE_catact_2NH PTE catact 2NH 0.1688986986
## 601 PTE_catact_2NH PTE catact 2NH 0.0224243247
## 602 PTE_catact_2NH PTE catact 2NH 0.0256556361
## 603 PTE_catact_2NH PTE catact 2NH -0.0935406092
## 604 PTE_catact_2NH PTE catact 2NH -0.2015806584
## 605 PTE_catact_2NH PTE catact 2NH 0.3272962613
## 606 PTE_catact_2NH PTE catact 2NH 0.4764903090
## 607 PTE_catact_2NH PTE catact 2NH 0.4376029757
## 608 PTE_catact_2NH PTE catact 2NH 0.2179017588
## 609 PTE_catact_2NH PTE catact 2NH -0.1814104041
## 610 PTE_catact_2NH PTE catact 2NH -0.0949573899
## 611 PTE_catact_2NH PTE catact 2NH 0.0065038763
## 612 PTE_catact_2NH PTE catact 2NH 1.0105759149
## 613 PTE_catact_2NH PTE catact 2NH -0.1228109891
## 614 PTE_catact_2NH PTE catact 2NH -0.0440976935
## 615 TEM_MIC_weinreich TEM MIC weinreich 0.9959252876
## 616 TEM_MIC_weinreich TEM MIC weinreich -0.6380093140
## 617 TEM_MIC_weinreich TEM MIC weinreich -0.5617283740
## 618 TEM_MIC_weinreich TEM MIC weinreich 0.8173978048
## 619 TEM_MIC_weinreich TEM MIC weinreich 0.4404367458
## 620 TEM_MIC_weinreich TEM MIC weinreich 0.6828096293
## 621 TEM_MIC_weinreich TEM MIC weinreich 2.9983253265
## 622 TEM_MIC_weinreich TEM MIC weinreich 0.7668805522
## 623 TEM_MIC_weinreich TEM MIC weinreich 0.0416191642
## 624 TEM_MIC_weinreich TEM MIC weinreich 0.1349017256
## 625 TEM_MIC_weinreich TEM MIC weinreich 2.0614142448
## 626 TEM_MIC_weinreich TEM MIC weinreich -1.1026904531
## 627 TEM_MIC_weinreich TEM MIC weinreich -0.2531919803
## 628 TEM_MIC_weinreich TEM MIC weinreich -0.9029548338
## 629 TEM_MIC_weinreich TEM MIC weinreich 0.2946085787
## 630 TEM_MIC_weinreich TEM MIC weinreich 0.1589193176
## 631 TEM_MIC_weinreich TEM MIC weinreich 0.0340169486
## 632 TEM_MIC_weinreich TEM MIC weinreich 0.5607717676
## 633 TEM_MIC_weinreich TEM MIC weinreich 0.0323045028
## 634 TEM_MIC_weinreich TEM MIC weinreich -0.7790332707
## 635 TEM_MIC_weinreich TEM MIC weinreich -0.0333338299
## 636 TEM_MIC_weinreich TEM MIC weinreich 0.4991308025
## 637 TEM_MIC_weinreich TEM MIC weinreich 0.4495140405
## 638 TEM_MIC_weinreich TEM MIC weinreich -0.2029847382
## 639 TEM_MIC_weinreich TEM MIC weinreich 0.1679882204
## 640 TEM_MIC_weinreich TEM MIC weinreich 1.5870060093
## 641 TEM_MIC_weinreich TEM MIC weinreich 0.2694733831
## 642 TEM_MIC_weinreich TEM MIC weinreich -1.1900805252
## 643 TEM_MIC_weinreich TEM MIC weinreich -0.9755374929
## 644 TEM_MIC_weinreich TEM MIC weinreich -0.8185917634
## 645 TEM_MIC_weinreich TEM MIC weinreich 0.1233783106
## traj_effects access access_no_idio access_mean access_w_mean access_w_idio
## 1 0.643452676 NA NA NA NA NA
## 2 1.383815366 2 2 2 0 0
## 3 1.910624405 3 1 1 -2 2
## 4 2.324282455 3 4 4 1 -1
## 5 2.103803721 0 0 0 0 0
## 6 2.158362492 2 6 2 0 -4
## 7 2.294466226 3 0 1 -2 3
## 8 2.222716471 4 4 1 -3 0
## 9 2.313867220 5 0 0 -5 5
## 10 2.227886705 4 4 4 0 0
## 11 2.071882007 3 1 1 -2 2
## 12 2.184691431 3 1 2 -1 2
## 13 2.278753601 4 2 2 -2 2
## 14 -3.031517051 0 3 1 1 -3
## 15 1.856124444 3 3 3 0 0
## 16 1.710963119 1 1 4 3 0
## 17 1.983175072 3 1 0 -3 2
## 18 1.146128036 2 2 2 0 0
## 19 2.342422681 3 3 2 -1 0
## 20 2.322219295 3 0 0 -3 3
## 21 2.324282455 4 1 0 -4 3
## 22 2.326335861 3 4 4 1 -1
## 23 2.086359831 3 3 0 -3 0
## 24 2.217483944 4 1 1 -3 3
## 25 2.049218023 3 2 2 -1 1
## 26 1.933993164 2 2 2 0 0
## 27 2.320146286 3 3 3 0 0
## 28 2.311753861 3 1 4 1 2
## 29 2.283301229 3 0 3 0 3
## 30 1.713490543 2 2 2 0 0
## 31 2.082785370 3 3 3 0 0
## 32 1.557507202 2 2 2 0 0
## 33 0.728353782 NA NA NA NA NA
## 34 -3.031517051 0 2 2 2 -2
## 35 0.717670503 1 1 1 0 0
## 36 2.324282455 4 4 2 -2 0
## 37 -0.798602876 1 1 0 -1 0
## 38 -0.422508200 0 4 4 4 -4
## 39 1.187520721 3 1 1 -2 2
## 40 1.973589623 4 4 4 0 0
## 41 1.363611980 3 0 1 -2 3
## 42 1.187520721 1 2 1 0 -1
## 43 -0.804100348 0 1 2 2 -1
## 44 -3.031517051 0 0 0 0 0
## 45 -0.277366077 0 0 0 0 0
## 46 0.706717782 1 2 2 1 -1
## 47 -0.543633967 0 2 2 2 -2
## 48 0.822821645 1 2 2 1 -1
## 49 0.307496038 NA NA NA NA NA
## 50 2.193124598 2 2 2 0 0
## 51 2.079181246 2 3 1 -1 -1
## 52 -0.228412519 0 0 0 0 0
## 53 2.049218023 2 1 1 -1 1
## 54 1.025305865 2 2 2 0 0
## 55 1.557507202 2 3 0 -2 -1
## 56 1.049218023 1 2 2 1 -1
## 57 1.855519156 NA NA NA NA NA
## 58 1.824125834 1 2 1 0 -1
## 59 -0.157390760 0 2 3 3 -2
## 60 1.692846919 1 2 1 0 -1
## 61 0.267171728 NA NA NA NA NA
## 62 1.964730921 2 2 1 -1 0
## 63 1.472756449 NA NA NA NA NA
## 64 0.352182518 NA NA NA NA NA
## 65 1.025305865 2 2 2 0 0
## 66 3.082785370 3 3 3 0 0
## 67 4.389166084 4 4 4 0 0
## 68 5.965671971 5 5 5 0 0
## 69 4.281033367 4 4 4 0 0
## 70 2.606381365 3 3 3 0 0
## 71 4.658964843 4 4 4 0 0
## 72 2.799340549 3 3 3 0 0
## 73 1.700703717 2 2 2 0 0
## 74 2.870988814 3 3 3 0 0
## 75 4.198657087 4 0 4 0 4
## 76 2.774516966 3 3 3 0 0
## 77 1.484299839 2 2 2 0 0
## 78 3.049218023 3 3 3 0 0
## 79 1.804139432 2 2 2 0 0
## 80 0.342422681 NA NA NA NA NA
## 81 1.828015064 2 2 2 0 0
## 82 3.017033339 3 3 3 0 0
## 83 4.582063363 4 4 4 0 0
## 84 3.354108439 3 3 3 0 0
## 85 1.324282455 2 2 2 0 0
## 86 3.675778342 3 3 3 0 0
## 87 1.283301229 2 2 2 0 0
## 88 1.130333768 NA NA NA NA NA
## 89 1.828015064 2 2 2 0 0
## 90 3.096910013 3 3 3 0 0
## 91 2.359835482 2 2 2 0 0
## 92 0.346352974 NA NA NA NA NA
## 93 2.738780558 2 2 2 0 0
## 94 0.620136055 NA NA NA NA NA
## 95 0.352182518 NA NA NA NA NA
## 96 1.025305865 2 2 2 0 0
## 97 3.082785370 3 3 3 0 0
## 98 3.962369336 4 4 4 0 0
## 99 5.230448921 5 5 5 0 0
## 100 4.281033367 4 4 4 0 0
## 101 2.037426498 3 3 3 0 0
## 102 4.117271296 4 4 4 0 0
## 103 2.799340549 3 3 3 0 0
## 104 1.700703717 2 2 2 0 0
## 105 2.981365509 3 3 3 0 0
## 106 4.110589710 4 2 4 0 2
## 107 2.774516966 3 3 3 0 0
## 108 1.643452676 2 2 2 0 0
## 109 2.450249108 3 3 3 0 0
## 110 1.804139432 2 2 2 0 0
## 111 0.342422681 NA NA NA NA NA
## 112 1.828015064 2 2 2 0 0
## 113 2.820857989 3 3 3 0 0
## 114 3.986771734 4 4 4 0 0
## 115 3.354108439 3 3 3 0 0
## 116 1.252853031 2 2 2 0 0
## 117 2.840733235 3 3 3 0 0
## 118 1.283301229 2 2 2 0 0
## 119 1.130333768 NA NA NA NA NA
## 120 2.301029996 2 2 2 0 0
## 121 3.056904851 3 3 3 0 0
## 122 2.359835482 2 2 2 0 0
## 123 0.359835482 NA NA NA NA NA
## 124 1.075546961 2 2 2 0 0
## 125 0.620136055 NA NA NA NA NA
## 126 0.271841607 NA NA NA NA NA
## 127 1.206825876 2 2 2 0 0
## 128 0.755112266 2 2 2 0 0
## 129 1.107209970 3 1 2 -1 2
## 130 1.240549248 3 3 3 0 0
## 131 -0.395773947 1 1 1 0 0
## 132 -0.094743951 2 2 2 0 0
## 133 0.980457892 2 2 2 0 0
## 134 0.994317153 NA NA NA NA NA
## 135 0.551449998 1 1 1 0 0
## 136 0.857935265 2 1 3 1 1
## 137 0.987666265 1 2 2 1 -1
## 138 -0.787812396 NA NA NA NA NA
## 139 -0.153044675 1 1 1 0 0
## 140 0.668385917 NA NA NA NA NA
## 141 0.930949031 NA NA NA NA NA
## 142 1.158362492 2 2 2 0 0
## 143 1.206825876 3 3 3 0 0
## 144 0.544068044 2 2 2 0 0
## 145 2.960946196 5 5 5 0 0
## 146 2.311753861 4 3 3 -1 1
## 147 0.176091259 2 2 2 0 0
## 148 2.079181246 3 3 3 0 0
## 149 2.204119983 3 1 3 0 2
## 150 1.100370545 2 2 2 0 0
## 151 0.110589710 1 1 1 0 0
## 152 1.392696953 4 4 4 0 0
## 153 0.708420900 0 0 1 1 0
## 154 -0.331614083 1 1 1 0 0
## 155 0.348304863 2 0 2 0 2
## 156 0.710963119 1 2 2 1 -1
## 157 -0.031984286 NA NA NA NA NA
## 158 -0.144480844 0 0 2 2 0
## 159 -0.204119983 0 0 0 0 0
## 160 -0.173277480 2 3 3 1 -1
## 161 -0.455931956 1 1 1 0 0
## 162 0.071882007 3 4 3 0 -1
## 163 -0.155522824 2 1 2 0 1
## 164 -0.118045029 2 3 3 1 -1
## 165 0.326335861 3 3 3 0 0
## 166 -0.196542884 1 1 2 1 0
## 167 -0.131355562 2 0 0 -2 2
## 168 -0.402304814 1 3 3 2 -2
## 169 0.260071388 3 1 3 0 2
## 170 -0.071604148 3 4 4 1 -1
## 171 -0.081445469 2 0 1 -1 2
## 172 0.459392488 3 3 3 0 0
## 173 -0.177178355 0 0 0 0 0
## 174 -0.077274542 0 0 0 0 0
## 175 -0.343901798 0 0 0 0 0
## 176 -0.661543506 0 2 2 2 -2
## 177 -0.368556231 1 1 1 0 0
## 178 -0.080398976 2 3 3 1 -1
## 179 -0.534617149 0 0 0 0 0
## 180 -0.151810883 1 3 3 2 -2
## 181 -0.090443971 0 0 0 0 0
## 182 -0.241845378 0 0 0 0 0
## 183 -0.542118103 0 0 0 0 0
## 184 -0.232844134 1 3 3 2 -2
## 185 -0.073657553 1 1 0 -1 0
## 186 -0.501689446 0 1 1 1 -1
## 187 0.037426498 2 1 1 -1 1
## 188 -0.059981845 0 0 2 2 0
## 189 -0.012333735 NA NA NA NA NA
## 190 -0.053056729 0 0 0 0 0
## 191 0.004321374 3 0 0 -3 3
## 192 0.235528447 2 4 4 2 -2
## 193 0.537819095 4 2 2 -2 2
## 194 0.017033339 3 4 4 1 -1
## 195 0.423245874 2 2 2 0 0
## 196 0.688419822 4 4 4 0 0
## 197 -0.095284455 0 0 0 0 0
## 198 -0.026872146 0 0 2 2 0
## 199 0.296665190 2 2 1 -1 0
## 200 0.523746467 3 4 4 1 -1
## 201 0.033423755 3 1 1 -2 2
## 202 0.457881897 2 1 2 0 1
## 203 0.542825427 2 3 3 1 -1
## 204 -0.093126465 0 0 0 0 0
## 205 -0.010995384 NA NA NA NA NA
## 206 -0.085656843 0 0 0 0 0
## 207 0.158362492 1 1 1 0 0
## 208 0.434568904 2 4 4 2 -2
## 209 -0.006123085 3 0 0 -3 3
## 210 0.334453751 1 1 1 0 0
## 211 0.610660163 3 2 2 -1 1
## 212 -0.069050969 0 0 1 1 0
## 213 -0.008773924 NA NA NA NA NA
## 214 0.212187604 1 1 1 0 0
## 215 0.488550717 2 2 2 0 0
## 216 -0.034798299 1 0 1 0 1
## 217 0.421603927 NA NA NA NA NA
## 218 0.582063363 2 1 2 0 1
## 219 -0.054039296 NA NA NA NA NA
## 220 0.056904851 NA NA NA NA NA
## 221 0.075546961 2 1 2 0 1
## 222 -0.083546051 1 1 0 -1 0
## 223 -0.024108864 2 2 2 0 0
## 224 -0.077274542 1 4 4 3 -3
## 225 0.734799830 4 6 6 2 -2
## 226 0.979092901 7 4 7 0 3
## 227 -0.144480844 2 0 4 2 2
## 228 0.737987326 4 4 4 0 0
## 229 0.975891136 5 5 6 1 0
## 230 -0.171340103 2 2 4 2 0
## 231 0.004321374 3 4 4 1 -1
## 232 0.758911892 4 2 3 -1 2
## 233 0.915927212 5 6 6 1 -1
## 234 -0.089375595 1 1 1 0 0
## 235 0.804139432 4 4 4 0 0
## 236 0.984527313 5 5 3 -2 0
## 237 -0.208309351 0 0 0 0 0
## 238 0.075546961 3 1 2 -1 2
## 239 -0.053547735 3 3 3 0 0
## 240 0.434568904 4 2 2 -2 2
## 241 0.598790507 3 6 5 2 -3
## 242 0.004321374 5 5 3 -2 0
## 243 0.374748346 2 3 4 2 -1
## 244 0.752048448 4 3 3 -1 1
## 245 0.004321374 3 3 4 1 0
## 246 -0.071604148 2 0 0 -2 2
## 247 0.396199347 2 4 4 2 -2
## 248 0.668385917 4 2 2 -2 2
## 249 -0.022733788 4 4 4 0 0
## 250 0.413299764 2 1 2 0 1
## 251 0.762678564 4 4 4 0 0
## 252 -0.029653124 2 2 0 -2 0
## 253 0.060697840 2 1 1 -1 1
## 254 0.033423755 2 1 0 -2 1
## 255 0.068185862 4 4 4 0 0
## 256 0.661812686 4 2 3 -1 2
## 257 0.874481818 6 6 6 0 0
## 258 -0.024108864 2 1 4 2 1
## 259 0.691081492 3 4 4 1 -1
## 260 0.831869774 4 4 4 0 0
## 261 -0.083546051 1 3 3 2 -2
## 262 0.041392685 2 0 1 -1 2
## 263 0.647382970 3 4 4 1 -1
## 264 0.867467488 5 4 2 -3 1
## 265 0.029383778 2 4 4 2 -2
## 266 0.736396502 3 3 3 0 0
## 267 0.848804701 4 4 3 -1 0
## 268 -0.076238039 1 1 0 -1 0
## 269 -0.016373713 0 1 2 2 -1
## 270 -0.181114585 0 0 0 0 0
## 271 0.530199698 2 4 4 2 -2
## 272 0.654176542 4 2 2 -2 2
## 273 -0.067019178 2 3 3 1 -1
## 274 0.564666064 2 2 2 0 0
## 275 0.712649702 3 4 4 1 -1
## 276 -0.034798299 2 0 0 -2 2
## 277 -0.151810883 0 1 2 2 -1
## 278 0.562292864 2 1 2 0 1
## 279 0.632457292 3 4 4 1 -1
## 280 -0.166215625 0 0 0 0 0
## 281 0.619093331 2 2 2 0 0
## 282 0.745074792 3 3 3 0 0
## 283 -0.040481623 1 1 0 -1 0
## 284 -0.071604148 NA NA NA NA NA
## 285 -0.013676223 1 0 1 0 1
## 286 -0.077274542 0 2 3 3 -2
## 287 -0.077274542 3 0 0 -3 3
## 288 0.459392488 4 3 4 0 1
## 289 0.636487896 4 4 6 2 0
## 290 -0.019088062 3 5 3 0 -2
## 291 -0.065501549 2 1 1 -1 1
## 292 0.521138084 3 5 2 -1 -2
## 293 0.113943352 2 0 1 -1 2
## 294 -0.155522824 1 3 3 2 -2
## 295 0.453318340 3 3 3 0 0
## 296 0.732393760 5 5 5 0 0
## 297 -0.107905397 2 0 0 -2 2
## 298 0.029383778 2 1 1 -1 1
## 299 0.526339277 4 2 2 -2 2
## 300 -0.010995384 1 3 3 2 -2
## 301 -0.074172425 0 1 0 0 -1
## 302 -0.166215625 1 2 2 1 -1
## 303 -0.208309351 1 1 0 -1 0
## 304 0.409933123 2 5 5 3 -3
## 305 -0.071604148 2 0 0 -2 2
## 306 -0.372634143 0 0 0 0 0
## 307 0.423245874 2 2 2 0 0
## 308 0.167317335 2 3 3 1 -1
## 309 -0.250263684 1 0 0 -1 1
## 310 -0.111259039 2 2 2 0 0
## 311 0.557507202 4 2 2 -2 2
## 312 -0.196542884 2 2 2 0 0
## 313 -0.327902142 0 1 1 1 -1
## 314 0.481442629 2 1 2 0 1
## 315 0.143014800 2 0 2 0 2
## 316 -0.012333735 NA NA NA NA NA
## 317 0.113943352 2 1 1 -1 1
## 318 0.103803721 2 3 3 1 -1
## 319 0.636487896 3 4 3 0 -1
## 320 0.868644438 5 5 2 -3 0
## 321 0.064457989 2 2 2 0 0
## 322 0.488550717 3 3 3 0 0
## 323 0.667452953 4 1 2 -2 3
## 324 0.245512668 3 3 3 0 0
## 325 -0.101823517 1 0 1 0 1
## 326 0.708420900 3 3 3 0 0
## 327 0.507855872 3 2 3 0 1
## 328 -0.065501549 2 2 2 0 0
## 329 0.463892989 1 1 2 1 0
## 330 0.503790683 2 3 2 0 -1
## 331 0.004321374 2 0 0 -2 2
## 332 0.021189299 NA NA NA NA NA
## 333 -0.141462802 1 1 0 -1 0
## 334 0.537819095 3 3 3 0 0
## 335 0.665580991 4 1 2 -2 3
## 336 -0.065501549 2 2 2 0 0
## 337 0.477121255 1 2 1 0 -1
## 338 0.654176542 3 3 3 0 0
## 339 0.209515015 2 1 1 -1 1
## 340 -0.301899454 NA NA NA NA NA
## 341 0.505149978 2 1 2 0 1
## 342 0.367355921 1 3 3 2 -2
## 343 -0.292429824 1 0 0 -1 1
## 344 0.480006943 NA NA NA NA NA
## 345 0.599883072 2 1 1 -1 1
## 346 -0.078313525 NA NA NA NA NA
## 347 0.056904851 NA NA NA NA NA
## 348 0.086359831 2 1 2 0 1
## 349 0.008600172 1 3 2 1 -2
## 350 0.029383778 2 1 2 0 1
## 351 -0.166215625 2 2 0 -2 0
## 352 0.979548375 5 6 6 1 -1
## 353 1.004321374 7 0 7 0 7
## 354 0.155336037 5 6 6 1 -1
## 355 0.935003151 5 3 2 -3 2
## 356 0.929929560 5 6 6 1 -1
## 357 0.723455672 5 0 2 -3 5
## 358 -0.173277480 0 0 0 0 0
## 359 0.941511433 5 5 2 -3 0
## 360 0.945468585 5 6 6 1 -1
## 361 0.103803721 4 0 0 -4 4
## 362 0.696356389 3 4 3 0 -1
## 363 0.836324116 4 1 1 -3 3
## 364 0.372912003 4 4 4 0 0
## 365 0.225309282 3 3 3 0 0
## 366 -0.101823517 3 1 1 -2 2
## 367 0.727541257 4 5 2 -2 -1
## 368 0.816903839 5 6 6 1 -1
## 369 -0.018634491 3 0 0 -3 3
## 370 0.627365857 3 3 4 1 0
## 371 0.851869601 5 0 2 -3 5
## 372 0.397940009 4 4 4 0 0
## 373 -0.118045029 2 2 2 0 0
## 374 0.705863712 3 4 4 1 -1
## 375 0.803457116 4 1 3 -1 3
## 376 -0.095284455 2 4 4 2 -2
## 377 0.582063363 2 1 2 0 1
## 378 0.824776462 4 4 4 0 0
## 379 0.313867220 3 1 1 -2 2
## 380 0.060697840 2 1 1 -1 1
## 381 0.146128036 2 3 3 1 -1
## 382 -0.224753740 1 0 1 0 1
## 383 1.000000000 5 1 1 -4 4
## 384 0.912753304 4 6 6 2 -2
## 385 0.107209970 4 0 0 -4 4
## 386 0.795880017 3 3 4 1 0
## 387 0.883093359 4 0 0 -4 4
## 388 0.359835482 4 4 4 0 0
## 389 -0.144480844 1 2 1 0 -1
## 390 0.649334859 1 4 4 3 -3
## 391 0.956648579 5 1 1 -4 4
## 392 0.021189299 3 4 4 1 -1
## 393 0.841359470 3 3 2 -1 0
## 394 0.918030337 4 4 4 0 0
## 395 0.278753601 3 1 1 -2 2
## 396 0.178976947 2 2 2 0 0
## 397 -0.287350298 0 2 2 2 -2
## 398 0.786751422 4 1 2 -2 3
## 399 0.800717078 5 1 1 -4 4
## 400 0.012837225 3 4 3 0 -1
## 401 0.745074792 3 3 3 0 0
## 402 0.602059991 1 4 4 3 -3
## 403 0.348304863 3 1 1 -2 2
## 404 -0.192464972 1 1 0 -1 0
## 405 0.720159303 3 3 3 0 0
## 406 0.758154622 4 4 4 0 0
## 407 -0.016373713 3 0 0 -3 3
## 408 0.671172843 2 2 2 0 0
## 409 0.742725131 3 1 1 -2 2
## 410 -0.449771647 0 1 1 1 -1
## 411 0.075546961 NA NA NA NA NA
## 412 0.264817823 2 1 1 -1 1
## 413 -0.019996628 NA NA NA NA NA
## 414 0.113943352 NA NA NA NA NA
## 415 0.045322979 0 2 2 2 -2
## 416 0.193124598 2 2 3 1 0
## 417 0.060697840 1 1 1 0 0
## 418 0.424881637 2 2 2 0 0
## 419 1.064457989 3 2 3 0 1
## 420 1.604226053 4 3 4 0 1
## 421 1.041392685 2 3 3 1 -1
## 422 0.356025857 NA NA NA NA NA
## 423 1.041392685 2 2 2 0 0
## 424 1.484299839 3 3 3 0 0
## 425 1.139879086 2 1 2 0 1
## 426 0.383815366 NA NA NA NA NA
## 427 0.416640507 2 1 2 0 1
## 428 0.938019097 3 3 2 -1 0
## 429 1.365487985 4 4 4 0 0
## 430 1.146128036 3 2 3 0 1
## 431 0.802773725 2 2 2 0 0
## 432 1.093421685 3 3 3 0 0
## 433 0.773054693 2 2 2 0 0
## 434 -0.012333735 NA NA NA NA NA
## 435 0.513217600 2 1 2 0 1
## 436 1.041392685 3 3 3 0 0
## 437 0.068185862 1 1 2 1 0
## 438 0.385606274 NA NA NA NA NA
## 439 0.770852012 2 2 2 0 0
## 440 0.139879086 NA NA NA NA NA
## 441 0.008600172 NA NA NA NA NA
## 442 0.195899652 1 2 1 0 -1
## 443 0.462397998 3 2 2 -1 1
## 444 0.691965103 3 3 3 0 0
## 445 1.012837225 4 4 4 0 0
## 446 0.755874856 3 3 3 0 0
## 447 0.332438460 2 2 2 0 0
## 448 0.238046103 2 0 1 -1 2
## 449 0.100370545 0 2 2 2 -2
## 450 0.017033339 2 1 2 0 1
## 451 0.374748346 2 2 2 0 0
## 452 0.764922985 3 3 3 0 0
## 453 0.146128036 2 2 3 1 0
## 454 0.149219113 1 2 1 0 -1
## 455 0.217483944 2 2 2 0 0
## 456 0.158362492 2 2 0 -2 0
## 457 0.086359831 2 1 2 0 1
## 458 0.472756449 2 2 2 0 0
## 459 0.758154622 3 3 3 0 0
## 460 0.997823081 4 5 5 1 -1
## 461 1.507855872 5 4 5 0 1
## 462 1.260071388 4 4 4 0 0
## 463 0.713490543 3 2 2 -1 1
## 464 1.322219295 4 5 4 0 -1
## 465 0.915399835 3 3 3 0 0
## 466 0.008600172 1 3 0 -1 -2
## 467 0.589949601 4 3 2 -2 1
## 468 0.898725182 4 5 5 1 -1
## 469 0.274157849 2 2 1 -1 0
## 470 0.570542940 3 2 3 0 1
## 471 0.840106094 3 2 3 0 1
## 472 0.365487985 2 3 3 1 -1
## 473 -0.008773924 NA NA NA NA NA
## 474 0.683947131 2 1 2 0 1
## 475 0.820201459 3 3 3 0 0
## 476 1.113943352 4 3 4 0 1
## 477 1.568201724 5 5 5 0 0
## 478 1.568201724 4 4 4 0 0
## 479 0.841984805 3 3 3 0 0
## 480 1.527629901 4 3 4 0 1
## 481 1.361727836 3 3 3 0 0
## 482 -0.014573526 0 0 0 0 0
## 483 0.478566496 1 3 1 0 -2
## 484 0.980003372 3 3 2 -1 0
## 485 0.281033367 2 2 2 0 0
## 486 0.539076099 2 1 2 0 1
## 487 0.898176483 3 3 3 0 0
## 488 0.380211242 2 1 2 0 1
## 489 0.004321374 NA NA NA NA NA
## 490 0.152288344 2 2 1 -1 0
## 491 0.621176282 3 3 3 0 0
## 492 0.611723308 2 4 4 2 -2
## 493 0.501059262 4 0 1 -3 4
## 494 0.376576957 2 3 4 2 -1
## 495 0.089905111 2 3 3 1 -1
## 496 0.243038049 3 4 4 1 -1
## 497 0.278753601 3 3 3 0 0
## 498 0.557507202 2 2 2 0 0
## 499 0.557507202 3 2 2 -1 1
## 500 0.460897843 2 4 4 2 -2
## 501 0.369215857 2 2 1 -1 0
## 502 0.004321374 1 2 0 -1 -1
## 503 0.110589710 3 0 0 -3 3
## 504 -0.031517051 0 2 2 2 -2
## 505 0.045322979 NA NA NA NA NA
## 506 0.506505032 2 2 1 -1 0
## 507 0.654176542 3 2 2 -1 1
## 508 0.442479769 1 4 4 3 -3
## 509 0.466867620 2 2 1 -1 0
## 510 0.086359831 2 2 2 0 0
## 511 0.170261715 3 3 2 -1 0
## 512 0.155336037 2 2 2 0 0
## 513 0.432969291 NA NA NA NA NA
## 514 0.403120521 1 2 2 1 -1
## 515 0.481442629 3 1 1 -2 2
## 516 0.352182518 1 2 1 0 -1
## 517 0.025305865 NA NA NA NA NA
## 518 0.033423755 1 2 2 1 -1
## 519 0.041392685 NA NA NA NA NA
## 520 0.056904851 NA NA NA NA NA
## 521 0.064457989 2 2 2 0 0
## 522 -0.008773924 0 3 2 2 -3
## 523 1.170261715 3 3 3 0 0
## 524 1.130333768 3 3 2 -1 0
## 525 1.212187604 4 6 4 0 -2
## 526 1.232996110 4 2 3 -1 2
## 527 0.117271296 3 0 2 -1 3
## 528 0.212187604 2 2 0 -2 0
## 529 0.269512944 3 4 4 1 -1
## 530 1.336459734 3 3 3 0 0
## 531 1.332438460 3 4 4 1 -1
## 532 1.276461804 3 2 2 -1 1
## 533 1.414973348 4 4 4 0 0
## 534 -0.006563770 0 0 0 0 0
## 535 0.053078443 1 4 2 1 -3
## 536 0.201397124 3 1 2 -1 2
## 537 0.133538908 2 2 0 -2 0
## 538 1.123851641 3 3 2 -1 0
## 539 1.093421685 3 4 4 1 -1
## 540 1.110589710 4 2 2 -2 2
## 541 0.967079734 2 4 4 2 -2
## 542 0.127104798 2 2 3 1 0
## 543 0.243038049 4 4 4 0 0
## 544 0.235528447 3 3 3 0 0
## 545 0.993876915 2 2 2 0 0
## 546 1.071882007 3 2 2 -1 1
## 547 1.217483944 4 4 4 0 0
## 548 1.149219113 3 2 2 -1 1
## 549 0.012837225 0 2 1 1 -2
## 550 0.075546961 3 0 0 -3 3
## 551 0.053078443 1 2 2 1 -1
## 552 0.613841822 NA NA NA NA NA
## 553 2.717670503 2 2 2 0 0
## 554 2.401400541 2 3 2 0 -1
## 555 2.799340549 3 3 2 -1 0
## 556 3.198657087 4 5 5 1 -1
## 557 2.999130541 1 1 1 0 0
## 558 3.107209970 5 5 5 0 0
## 559 3.240549248 4 2 1 -3 2
## 560 3.100370545 4 5 4 0 -1
## 561 2.831229694 4 2 2 -2 2
## 562 2.912222057 3 3 3 0 0
## 563 3.117271296 4 4 4 0 0
## 564 3.000000000 4 1 5 1 3
## 565 2.685741739 3 3 3 0 0
## 566 3.021189299 3 3 3 0 0
## 567 2.812244697 3 2 3 0 1
## 568 2.187520721 2 3 3 1 -1
## 569 0.572871602 1 1 1 0 0
## 570 0.570542940 1 3 3 2 -2
## 571 1.685741739 4 2 4 0 2
## 572 2.017033339 4 2 4 0 2
## 573 0.550228353 3 1 4 1 2
## 574 1.672097858 2 2 2 0 0
## 575 2.190331698 4 4 4 0 0
## 576 0.532754379 2 2 3 1 0
## 577 0.651278014 2 2 1 -1 0
## 578 1.664641976 2 2 2 0 0
## 579 0.474216264 0 3 1 1 -3
## 580 0.492760389 2 3 2 0 -1
## 581 1.951823035 2 2 2 0 0
## 582 1.367355921 2 2 2 0 0
## 583 0.481442629 1 1 1 0 0
## 584 0.820857989 NA NA NA NA NA
## 585 0.820201459 1 1 1 0 0
## 586 1.382017043 3 3 2 -1 0
## 587 2.950851459 4 4 3 -1 0
## 588 3.071882007 5 5 5 0 0
## 589 1.596597096 4 2 2 -2 2
## 590 2.113943352 2 3 2 0 -1
## 591 2.600972896 4 4 1 -3 0
## 592 0.945960704 2 3 1 -1 -1
## 593 1.267171728 2 2 2 0 0
## 594 2.725094521 3 3 3 0 0
## 595 2.634477270 3 4 4 1 -1
## 596 1.488550717 3 3 3 0 0
## 597 2.209515015 2 2 2 0 0
## 598 1.791690649 2 2 2 0 0
## 599 0.956648579 2 1 2 0 1
## 600 -0.193820026 NA NA NA NA NA
## 601 0.149219113 1 1 1 0 0
## 602 1.336459734 2 2 2 0 0
## 603 1.683047038 4 4 4 0 0
## 604 0.068185862 1 2 1 0 -1
## 605 1.235528447 1 1 2 1 0
## 606 1.630427875 3 1 2 -1 2
## 607 -0.194499142 0 0 2 2 0
## 608 0.274157849 NA NA NA NA NA
## 609 1.367355921 1 2 2 1 -1
## 610 1.158362492 2 2 2 0 0
## 611 0.371067862 2 1 1 -1 1
## 612 1.419955748 NA NA NA NA NA
## 613 0.691081492 1 1 1 0 0
## 614 -0.065501549 NA NA NA NA NA
## 615 0.178976947 NA NA NA NA NA
## 616 0.269512944 2 1 0 -2 1
## 617 3.611723308 3 3 3 0 0
## 618 3.611723308 2 2 2 0 0
## 619 -0.090979146 NA NA NA NA NA
## 620 2.561101384 2 1 2 0 1
## 621 1.201397124 NA NA NA NA NA
## 622 0.000000000 NA NA NA NA NA
## 623 1.201397124 2 1 2 0 1
## 624 0.962369336 1 3 3 2 -2
## 625 4.518513940 4 1 4 0 3
## 626 4.378397901 3 3 3 0 0
## 627 1.201397124 2 0 0 -2 2
## 628 3.611723308 3 3 3 0 0
## 629 2.416640507 2 2 2 0 0
## 630 0.000000000 NA NA NA NA NA
## 631 0.311753861 2 2 2 0 0
## 632 0.311753861 3 3 3 0 0
## 633 4.378397901 4 4 4 0 0
## 634 3.611723308 3 3 2 -1 0
## 635 0.000000000 2 0 0 -2 2
## 636 3.611723308 3 3 3 0 0
## 637 1.201397124 2 2 2 0 0
## 638 0.000000000 2 2 0 -2 0
## 639 1.371067862 3 3 3 0 0
## 640 1.201397124 3 0 3 0 3
## 641 4.668385917 5 4 5 0 1
## 642 4.285557309 3 4 4 1 -1
## 643 0.000000000 2 3 2 0 -1
## 644 3.611723308 4 4 4 0 0
## 645 3.611723308 3 2 3 0 1
What does accessibility look like in adaptive trajectories only?
adaptive_traj_access %>%
filter(likely)
## genotype avg pos mutations likely
## 1 x0xx00 -0.1243552470 p1|p26|p28 3 TRUE
## 2 00xx00 0.0301094048 p26|p28 2 TRUE
## 3 000x00 1.8555191557 p28 1 TRUE
## 4 xxxxx -0.4024022633 p21|p26|p28|p30|p94 5 TRUE
## 5 0xxxx 0.3612176043 p26|p28|p30|p94 4 TRUE
## 6 0xx0x -0.0241508318 p26|p28|p94 3 TRUE
## 7 00x00 1.1303337685 p28 1 TRUE
## 8 00x0x 0.6093656589 p28|p94 2 TRUE
## 9 xxxxx -0.4985709562 p21|p26|p28|p30|p94 5 TRUE
## 10 xxx0x -0.1734921898 p21|p26|p28|p94 4 TRUE
## 11 0xx0x -0.0241508318 p26|p28|p94 3 TRUE
## 12 00x00 1.1303337685 p28 1 TRUE
## 13 00x0x 0.6093656589 p28|p94 2 TRUE
## 14 0xx00 -0.0593328832 p193|p258 2 TRUE
## 15 0xx0x 0.0001438909 p193|p258|p273 3 TRUE
## 16 00x00 0.9943171527 p258 1 TRUE
## 17 xxxxx -0.6987406300 p72|p193|p258|p271|p273 5 TRUE
## 18 xxx0x 0.2309903363 p72|p193|p258|p273 4 TRUE
## 19 00000x0 0.4216039269 p225 1 TRUE
## 20 xxx00xx -0.2305882837 p41|p215|p219|p225|p227 5 TRUE
## 21 x0x00xx 0.0747386150 p41|p219|p225|p227 4 TRUE
## 22 x0000x0 0.1405845524 p41|p225 2 TRUE
## 23 x0000xx 0.0088692029 p41|p225|p227 3 TRUE
## 24 00000x0 0.4800069430 p225 1 TRUE
## 25 xxxxxxx 1.9130554790 p41|p215|p219|p222|p224|p225|p227 7 TRUE
## 26 xxx0xxx -0.7393002155 p41|p215|p219|p224|p225|p227 6 TRUE
## 27 x0x0xxx 0.9140294387 p41|p219|p224|p225|p227 5 TRUE
## 28 x0x00x0 0.1701738576 p41|p219|p225 3 TRUE
## 29 x0x00xx -0.5446666127 p41|p219|p225|p227 4 TRUE
## 30 x0000x0 0.1342610484 p41|p225 2 TRUE
## 31 xxxx 0.2724234489 p33|p72|p212|p213 4 TRUE
## 32 0x00 0.3560258572 p72 1 TRUE
## 33 0xxx -0.5502135651 p72|p212|p213 3 TRUE
## 34 0x0x 0.8038498576 p72|p213 2 TRUE
## 35 00x0x0 0.0333520857 p124|p156 2 TRUE
## 36 00x0xx -0.3439580192 p124|p156|p243 3 TRUE
## 37 0000x0 0.3856062736 p156 1 TRUE
## 38 xxxxxx 0.2371716121 p66|p76|p124|p132|p156|p243 6 TRUE
## 39 0xxxxx -0.1316049379 p76|p124|p132|p156|p243 5 TRUE
## 40 0xx0xx 0.3817071915 p76|p124|p156|p243 4 TRUE
## 41 000x00 0.4329692909 p158 1 TRUE
## 42 xx0x00 0.2148131438 p67|p142|p158 3 TRUE
## 43 xx0x0x -0.1873206652 p67|p142|p158|p215 4 TRUE
## 44 x00x00 0.5040027727 p67|p158 2 TRUE
## 45 xxx0x0 0.8786927259 p233|p254|p271|p306 4 TRUE
## 46 xx00x0 -1.0031636940 p233|p254|p306 3 TRUE
## 47 0x00x0 -0.0312987234 p254|p306 2 TRUE
## 48 0000x0 1.4199557485 p306 1 TRUE
## 49 00x0x 2.2313492364 p104|p238 2 TRUE
## 50 0000x 1.2013971243 p238 1 TRUE
## 51 0xxxx 1.9266775533 p42|p104|p182|p238 4 TRUE
## 52 0xx0x -1.4709889671 p42|p104|p238 3 TRUE
## 53 xxxxx 0.2694733831 p4205|p42|p104|p182|p238 5 TRUE
## unique_id enzyme_name measure_type cond means
## 1 DHFR_ic75_palmer DHFR ic75 palmer -0.37628762
## 2 DHFR_ic75_palmer DHFR ic75 palmer 0.51316879
## 3 DHFR_ic75_palmer DHFR ic75 palmer 0.02486007
## 4 DHFR_ki_trajg DHFR ki trajg -0.40240226
## 5 DHFR_ki_trajg DHFR ki trajg 0.16001647
## 6 DHFR_ki_trajg DHFR ki trajg -0.03088869
## 7 DHFR_ki_trajg DHFR ki trajg 1.39680945
## 8 DHFR_ki_trajg DHFR ki trajg -0.29783448
## 9 DHFR_ki_trajr DHFR ki trajr -0.49857096
## 10 DHFR_ki_trajr DHFR ki trajr -0.42277767
## 11 DHFR_ki_trajr DHFR ki trajr -0.45452917
## 12 DHFR_ki_trajr DHFR ki trajr 1.55984210
## 13 DHFR_ki_trajr DHFR ki trajr -0.07881130
## 14 MPH_catact_ZnPTM MPH catact ZnPTM -0.04544503
## 15 MPH_catact_ZnPTM MPH catact ZnPTM 0.13055704
## 16 MPH_catact_ZnPTM MPH catact ZnPTM 0.64194186
## 17 MPH_catact_ZnPTM MPH catact ZnPTM -0.69874063
## 18 MPH_catact_ZnPTM MPH catact ZnPTM -0.11837998
## 19 NfsA_ec50_2039 NfsA ec50 2039 0.47870667
## 20 NfsA_ec50_2039 NfsA ec50 2039 -0.41197263
## 21 NfsA_ec50_2039 NfsA ec50 2039 -0.03642342
## 22 NfsA_ec50_2039 NfsA ec50 2039 0.53230784
## 23 NfsA_ec50_2039 NfsA ec50 2039 -0.07627179
## 24 NfsA_ec50_3637 NfsA ec50 3637 0.60053817
## 25 NfsA_ec50_3637 NfsA ec50 3637 1.91305548
## 26 NfsA_ec50_3637 NfsA ec50 3637 0.21722752
## 27 NfsA_ec50_3637 NfsA ec50 3637 0.32566077
## 28 NfsA_ec50_3637 NfsA ec50 3637 -0.04276810
## 29 NfsA_ec50_3637 NfsA ec50 3637 -0.12431211
## 30 NfsA_ec50_3637 NfsA ec50 3637 0.29847925
## 31 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.27242345
## 32 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.92788736
## 33 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 -0.41400184
## 34 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.52980229
## 35 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.25754595
## 36 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.19574460
## 37 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.37836646
## 38 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.23717161
## 39 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.01301913
## 40 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.23085209
## 41 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.73061489
## 42 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.17401193
## 43 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.05144358
## 44 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.66294172
## 45 PTE_catact_2NH PTE catact 2NH -0.33352395
## 46 PTE_catact_2NH PTE catact 2NH -0.80218223
## 47 PTE_catact_2NH PTE catact 2NH -0.31271448
## 48 PTE_catact_2NH PTE catact 2NH 1.01057591
## 49 TEM_MIC_weinreich TEM MIC weinreich 0.81739780
## 50 TEM_MIC_weinreich TEM MIC weinreich 2.99832533
## 51 TEM_MIC_weinreich TEM MIC weinreich 2.06141424
## 52 TEM_MIC_weinreich TEM MIC weinreich -1.10269045
## 53 TEM_MIC_weinreich TEM MIC weinreich 0.26947338
## traj_effects access access_no_idio access_mean access_w_mean access_w_idio
## 1 2.3424227 3 3 2 -1 0
## 2 2.1931246 2 2 2 0 0
## 3 1.8555192 NA NA NA NA NA
## 4 5.9656720 5 5 5 0 0
## 5 4.5820634 4 4 4 0 0
## 6 3.3541084 3 3 3 0 0
## 7 1.1303338 NA NA NA NA NA
## 8 2.3598355 2 2 2 0 0
## 9 5.2304489 5 5 5 0 0
## 10 4.2810334 4 4 4 0 0
## 11 3.3541084 3 3 3 0 0
## 12 1.1303338 NA NA NA NA NA
## 13 2.3598355 2 2 2 0 0
## 14 1.2068259 2 2 2 0 0
## 15 1.2405492 3 3 3 0 0
## 16 0.9943172 NA NA NA NA NA
## 17 2.9609462 5 5 5 0 0
## 18 2.3117539 4 3 3 -1 1
## 19 0.4216039 NA NA NA NA NA
## 20 0.9845273 5 5 3 -2 0
## 21 0.8488047 4 4 3 -1 0
## 22 0.6190933 2 2 2 0 0
## 23 0.7450748 3 3 3 0 0
## 24 0.4800069 NA NA NA NA NA
## 25 1.0043214 7 0 7 0 7
## 26 0.9454686 5 6 6 1 -1
## 27 0.9566486 5 1 1 -4 4
## 28 0.8413595 3 3 2 -1 0
## 29 0.9180303 4 4 4 0 0
## 30 0.6711728 2 2 2 0 0
## 31 1.6042261 4 3 4 0 1
## 32 0.3560259 NA NA NA NA NA
## 33 1.4842998 3 3 3 0 0
## 34 1.1398791 2 1 2 0 1
## 35 0.8027737 2 2 2 0 0
## 36 1.0934217 3 3 3 0 0
## 37 0.3856063 NA NA NA NA NA
## 38 1.5078559 5 4 5 0 1
## 39 1.5682017 5 5 5 0 0
## 40 1.5276299 4 3 4 0 1
## 41 0.4329693 NA NA NA NA NA
## 42 1.3364597 3 3 3 0 0
## 43 1.4149733 4 4 4 0 0
## 44 0.9938769 2 2 2 0 0
## 45 3.2405492 4 2 1 -3 2
## 46 3.0211893 3 3 3 0 0
## 47 2.2095150 2 2 2 0 0
## 48 1.4199557 NA NA NA NA NA
## 49 3.6117233 2 2 2 0 0
## 50 1.2013971 NA NA NA NA NA
## 51 4.5185139 4 1 4 0 3
## 52 4.3783979 3 3 3 0 0
## 53 4.6683859 5 4 5 0 1
And again for non-adaptive trajectories
adaptive_traj_access %>%
filter(!likely)
## genotype avg pos mutations likely
## 1 x00000 0.643452676 p1 1 FALSE
## 2 xx0000 0.012008907 p1|p21 2 FALSE
## 3 xxx000 4.091500551 p1|p21|p26 3 FALSE
## 4 xxxx00 -3.440073960 p1|p21|p26|p28 4 FALSE
## 5 xxxxx0 0.212731913 p1|p21|p26|p28|p30 5 FALSE
## 6 xxxxxx -7.189135589 p1|p21|p26|p28|p30|p94 6 FALSE
## 7 xxxx0x 10.435863780 p1|p21|p26|p28|p94 5 FALSE
## 8 xxx0x0 -3.820446613 p1|p21|p26|p30 4 FALSE
## 9 xxx0xx 2.575532781 p1|p21|p26|p30|p94 5 FALSE
## 10 xxx00x -5.302828501 p1|p21|p26|p94 4 FALSE
## 11 xx0x00 0.793878371 p1|p21|p28 3 FALSE
## 12 xx0xx0 1.995805108 p1|p21|p28|p30 4 FALSE
## 13 xx0xxx 3.128986244 p1|p21|p28|p30|p94 5 FALSE
## 14 xx0x0x -5.214086328 p1|p21|p28|p94 4 FALSE
## 15 xx00x0 -0.308921060 p1|p21|p30 3 FALSE
## 16 xx00xx 1.369861023 p1|p21|p30|p94 4 FALSE
## 17 xx000x 1.063593767 p1|p21|p94 3 FALSE
## 18 x0x000 0.195179321 p1|p26 2 FALSE
## 19 x0xxx0 0.256637693 p1|p26|p28|p30 4 FALSE
## 20 x0xxxx 0.513576086 p1|p26|p28|p30|p94 5 FALSE
## 21 x0xx0x -1.104230472 p1|p26|p28|p94 4 FALSE
## 22 x0x0x0 -0.580444171 p1|p26|p30 3 FALSE
## 23 x0x0xx 0.207117216 p1|p26|p30|p94 4 FALSE
## 24 x0x00x 0.720069926 p1|p26|p94 3 FALSE
## 25 x00x00 -0.564978668 p1|p28 2 FALSE
## 26 x00xx0 -0.385319694 p1|p28|p30 3 FALSE
## 27 x00xxx 2.230706310 p1|p28|p30|p94 4 FALSE
## 28 x00x0x 1.070682225 p1|p28|p94 3 FALSE
## 29 x000x0 0.802866138 p1|p30 2 FALSE
## 30 x000xx -0.769562442 p1|p30|p94 3 FALSE
## 31 x0000x -0.558701924 p1|p94 2 FALSE
## 32 0x0000 0.728353782 p21 1 FALSE
## 33 0xx000 -4.067366871 p21|p26 2 FALSE
## 34 0xxx00 3.259911211 p21|p26|p28 3 FALSE
## 35 0xxxx0 -0.109252622 p21|p26|p28|p30 4 FALSE
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## 361 x0xx0xx 0.671911874 p41|p219|p222|p225|p227 5 FALSE
## 362 x0xx00x -0.528627695 p41|p219|p222|p227 4 FALSE
## 363 x0x0x00 -0.168218534 p41|p219|p224 3 FALSE
## 364 x0x0xx0 -0.292177414 p41|p219|p224|p225 4 FALSE
## 365 x0x0x0x -0.667037078 p41|p219|p224|p227 4 FALSE
## 366 x0x000x 0.629763626 p41|p219|p227 3 FALSE
## 367 x00x000 0.100882797 p41|p222 2 FALSE
## 368 x00xx00 -0.356204775 p41|p222|p224 3 FALSE
## 369 x00xxx0 0.313340140 p41|p222|p224|p225 4 FALSE
## 370 x00xxxx 0.333921164 p41|p222|p224|p225|p227 5 FALSE
## 371 x00xx0x -0.351760533 p41|p222|p224|p227 4 FALSE
## 372 x00x0x0 -0.024095160 p41|p222|p225 3 FALSE
## 373 x00x0xx -0.681111421 p41|p222|p225|p227 4 FALSE
## 374 x00x00x 0.409365174 p41|p222|p227 3 FALSE
## 375 x000x00 0.052529631 p41|p224 2 FALSE
## 376 x000xx0 -0.028686206 p41|p224|p225 3 FALSE
## 377 x000xxx -0.370871386 p41|p224|p225|p227 4 FALSE
## 378 x000x0x 0.594984602 p41|p224|p227 3 FALSE
## 379 x0000xx 0.380039133 p41|p225|p227 3 FALSE
## 380 x00000x -0.428362974 p41|p227 2 FALSE
## 381 00x0 0.075546961 p212 1 FALSE
## 382 00xx 0.209267490 p212|p213 2 FALSE
## 383 000x -0.019996628 p213 1 FALSE
## 384 x000 0.113943352 p33 1 FALSE
## 385 x0x0 -0.144167335 p33|p212 2 FALSE
## 386 x0xx -0.008220359 p33|p212|p213 3 FALSE
## 387 x00x -0.033248884 p33|p213 2 FALSE
## 388 xx00 -0.045087573 p33|p72 2 FALSE
## 389 xxx0 0.098376860 p33|p72|p212 3 FALSE
## 390 xx0x -0.134093297 p33|p72|p213 3 FALSE
## 391 0xx0 0.609819867 p72|p212 2 FALSE
## 392 00x000 0.383815366 p124 1 FALSE
## 393 00xx00 0.045158876 p124|p132 2 FALSE
## 394 00xxx0 -0.037524831 p124|p132|p156 3 FALSE
## 395 00xxxx -0.405716110 p124|p132|p156|p243 4 FALSE
## 396 00xx0x 0.399607691 p124|p132|p243 3 FALSE
## 397 00x00x 0.249360241 p124|p243 2 FALSE
## 398 000x00 -0.012333735 p132 1 FALSE
## 399 000xx0 0.139945062 p132|p156 2 FALSE
## 400 000xxx 0.202288837 p132|p156|p243 3 FALSE
## 401 000x0x -0.059359490 p132|p243 2 FALSE
## 402 0000xx 0.245366652 p156|p243 2 FALSE
## 403 00000x 0.139879086 p243 1 FALSE
## 404 x00000 0.008600172 p66 1 FALSE
## 405 x0x000 -0.196515885 p66|p124 2 FALSE
## 406 x0xx00 0.212906302 p66|p124|p132 3 FALSE
## 407 x0xxx0 -0.086542938 p66|p124|p132|p156 4 FALSE
## 408 x0xxxx 0.089397193 p66|p124|p132|p156|p243 5 FALSE
## 409 x0xx0x 0.010065898 p66|p124|p132|p243 4 FALSE
## 410 x0x0x0 -0.037432219 p66|p124|p156 3 FALSE
## 411 x0x0xx 0.426592258 p66|p124|p156|p243 4 FALSE
## 412 x0x00x -0.494651669 p66|p124|p243 3 FALSE
## 413 x00x00 0.020766903 p66|p132 2 FALSE
## 414 x00xx0 0.077151004 p66|p132|p156 3 FALSE
## 415 x00xxx 0.140288594 p66|p132|p156|p243 4 FALSE
## 416 x00x0x 0.038691866 p66|p132|p243 3 FALSE
## 417 x000x0 -0.244987333 p66|p156 2 FALSE
## 418 x000xx -0.326864140 p66|p156|p243 3 FALSE
## 419 x0000x 0.009883234 p66|p243 2 FALSE
## 420 xx0000 0.086533583 p66|p76 2 FALSE
## 421 xxx000 -0.109808551 p66|p76|p124 3 FALSE
## 422 xxxx00 0.008197600 p66|p76|p124|p132 4 FALSE
## 423 xxxxx0 -0.202103238 p66|p76|p124|p132|p156 5 FALSE
## 424 xxxx0x -0.100819874 p66|p76|p124|p132|p243 5 FALSE
## 425 xxx0x0 0.183795553 p66|p76|p124|p156 4 FALSE
## 426 xxx0xx -0.288691065 p66|p76|p124|p156|p243 5 FALSE
## 427 xxx00x 0.369371361 p66|p76|p124|p243 4 FALSE
## 428 xx0x00 -0.092726960 p66|p76|p132 3 FALSE
## 429 xx0xx0 0.074725318 p66|p76|p132|p156 4 FALSE
## 430 xx0xxx -0.323220456 p66|p76|p132|p156|p243 5 FALSE
## 431 xx0x0x 0.041115931 p66|p76|p132|p243 4 FALSE
## 432 xx00x0 0.181320419 p66|p76|p156 3 FALSE
## 433 xx00xx 0.347183922 p66|p76|p156|p243 4 FALSE
## 434 xx000x -0.119740246 p66|p76|p243 3 FALSE
## 435 0x0000 -0.008773924 p76 1 FALSE
## 436 0xx000 0.308905689 p76|p124 2 FALSE
## 437 0xxx00 0.096895054 p76|p124|p132 3 FALSE
## 438 0xxxx0 0.227939051 p76|p124|p132|p156 4 FALSE
## 439 0xxx0x -0.236009858 p76|p124|p132|p243 4 FALSE
## 440 0xx0x0 -0.423164435 p76|p124|p156 3 FALSE
## 441 0xx00x 0.039435298 p76|p124|p243 3 FALSE
## 442 0x0x00 0.006534133 p76|p132 2 FALSE
## 443 0x0xx0 -0.194655063 p76|p132|p156 3 FALSE
## 444 0x0xxx 0.033425928 p76|p132|p156|p243 4 FALSE
## 445 0x0x0x -0.034018783 p76|p132|p243 3 FALSE
## 446 0x00x0 0.162243750 p76|p156 2 FALSE
## 447 0x00xx -0.275251433 p76|p156|p243 3 FALSE
## 448 0x000x 0.249106080 p76|p243 2 FALSE
## 449 0x0000 0.004321374 p142 1 FALSE
## 450 0xx000 0.102643992 p142|p154 2 FALSE
## 451 0xxx00 -0.112510653 p142|p154|p158 3 FALSE
## 452 0xxxx0 -0.108859033 p142|p154|p158|p180 4 FALSE
## 453 0xxxxx 0.496024236 p142|p154|p158|p180|p215 5 FALSE
## 454 0xxx0x -0.191140649 p142|p154|p158|p215 4 FALSE
## 455 0xx0x0 -0.078114220 p142|p154|p180 3 FALSE
## 456 0xx0xx -0.122602702 p142|p154|p180|p215 4 FALSE
## 457 0xx00x 0.093683308 p142|p154|p215 3 FALSE
## 458 0x0x00 0.120216537 p142|p158 2 FALSE
## 459 0x0xx0 0.055154635 p142|p158|p180 3 FALSE
## 460 0x0xxx -0.242808451 p142|p158|p180|p215 4 FALSE
## 461 0x0x0x -0.030273461 p142|p158|p215 3 FALSE
## 462 0x00x0 -0.025305865 p142|p180 2 FALSE
## 463 0x00xx 0.175381557 p142|p180|p215 3 FALSE
## 464 0x000x -0.077231110 p142|p215 2 FALSE
## 465 00x000 0.045322979 p154 1 FALSE
## 466 00xx00 0.028212763 p154|p158 2 FALSE
## 467 00xxx0 0.161789293 p154|p158|p180 3 FALSE
## 468 00xxxx -0.338331862 p154|p158|p180|p215 4 FALSE
## 469 00xx0x -0.027471013 p154|p158|p215 3 FALSE
## 470 00x0x0 0.015730987 p154|p180 2 FALSE
## 471 00x0xx 0.007163621 p154|p180|p215 3 FALSE
## 472 00x00x 0.068620374 p154|p215 2 FALSE
## 473 000xx0 -0.055154635 p158|p180 2 FALSE
## 474 000xxx 0.192383675 p158|p180|p215 3 FALSE
## 475 000x0x -0.122179458 p158|p215 2 FALSE
## 476 0000x0 0.025305865 p180 1 FALSE
## 477 0000xx -0.033274795 p180|p215 2 FALSE
## 478 00000x 0.041392685 p215 1 FALSE
## 479 x00000 0.056904851 p67 1 FALSE
## 480 xx0000 0.003231764 p67|p142 2 FALSE
## 481 xxx000 -0.252509962 p67|p142|p154 3 FALSE
## 482 xxxx00 -0.033796120 p67|p142|p154|p158 4 FALSE
## 483 xxxxx0 0.021953947 p67|p142|p154|p158|p180 5 FALSE
## 484 xxxxxx -0.472613514 p67|p142|p154|p158|p180|p215 6 FALSE
## 485 xxxx0x 0.451943748 p67|p142|p154|p158|p215 5 FALSE
## 486 xxx0x0 0.237547682 p67|p142|p154|p180 4 FALSE
## 487 xxx0xx 0.069121497 p67|p142|p154|p180|p215 5 FALSE
## 488 xxx00x -0.058151521 p67|p142|p154|p215 4 FALSE
## 489 xx0xx0 -0.110226869 p67|p142|p158|p180 4 FALSE
## 490 xx0xxx 0.261891509 p67|p142|p158|p180|p215 5 FALSE
## 491 xx00x0 -0.001648267 p67|p142|p180 3 FALSE
## 492 xx00xx -0.319214623 p67|p142|p180|p215 4 FALSE
## 493 xx000x 0.217996653 p67|p142|p215 3 FALSE
## 494 x0x000 0.031311078 p67|p154 2 FALSE
## 495 x0xx00 0.025127906 p67|p154|p158 3 FALSE
## 496 x0xxx0 -0.307857857 p67|p154|p158|p180 4 FALSE
## 497 x0xxxx 0.574604487 p67|p154|p158|p180|p215 5 FALSE
## 498 x0xx0x -0.390459038 p67|p154|p158|p215 4 FALSE
## 499 x0x0x0 0.021902530 p67|p154|p180 3 FALSE
## 500 x0x0xx -0.059756054 p67|p154|p180|p215 4 FALSE
## 501 x0x00x 0.037195573 p67|p154|p215 3 FALSE
## 502 x00xx0 0.177227354 p67|p158|p180 3 FALSE
## 503 x00xxx -0.268660080 p67|p158|p180|p215 4 FALSE
## 504 x00x0x 0.281348063 p67|p158|p215 3 FALSE
## 505 x000x0 -0.069373492 p67|p180 2 FALSE
## 506 x000xx 0.099810939 p67|p180|p215 3 FALSE
## 507 x0000x -0.045219093 p67|p215 2 FALSE
## 508 x00000 0.613841822 p233 1 FALSE
## 509 xx0000 1.282970692 p233|p254 2 FALSE
## 510 xxx000 -0.468463239 p233|p254|p271 3 FALSE
## 511 xxxx00 0.196532755 p233|p254|p271|p272 4 FALSE
## 512 xxxxx0 -0.803129339 p233|p254|p271|p272|p306 5 FALSE
## 513 xxxxxx 0.272524024 p233|p254|p271|p272|p306|p313 6 FALSE
## 514 xxxx0x -0.717548373 p233|p254|p271|p272|p313 5 FALSE
## 515 xxx0xx -1.757566024 p233|p254|p271|p306|p313 5 FALSE
## 516 xxx00x 0.942550744 p233|p254|p271|p313 4 FALSE
## 517 xx0x00 -0.015040528 p233|p254|p272 3 FALSE
## 518 xx0xx0 -0.169875749 p233|p254|p272|p306 4 FALSE
## 519 xx0xxx 0.483694557 p233|p254|p272|p306|p313 5 FALSE
## 520 xx0x0x 0.406611059 p233|p254|p272|p313 4 FALSE
## 521 xx00xx 0.663515348 p233|p254|p306|p313 4 FALSE
## 522 xx000x -0.599042727 p233|p254|p313 3 FALSE
## 523 x0x000 0.152849806 p233|p271 2 FALSE
## 524 x0xx00 -0.108646144 p233|p271|p272 3 FALSE
## 525 x0xxx0 0.255939970 p233|p271|p272|p306 4 FALSE
## 526 x0xxxx 0.698522544 p233|p271|p272|p306|p313 5 FALSE
## 527 x0xx0x 0.288686765 p233|p271|p272|p313 4 FALSE
## 528 x0x0x0 -0.248147682 p233|p271|p306 3 FALSE
## 529 x0x0xx -0.048532267 p233|p271|p306|p313 4 FALSE
## 530 x0x00x 0.027459537 p233|p271|p313 3 FALSE
## 531 x00x00 -0.236721657 p233|p272 2 FALSE
## 532 x00xx0 0.002140425 p233|p272|p306 3 FALSE
## 533 x00xxx -0.937309431 p233|p272|p306|p313 4 FALSE
## 534 x00x0x -0.188529993 p233|p272|p313 3 FALSE
## 535 x000x0 -0.081974535 p233|p306 2 FALSE
## 536 x000xx 0.211304787 p233|p306|p313 3 FALSE
## 537 x0000x -0.066897645 p233|p313 2 FALSE
## 538 0x0000 0.820857989 p254 1 FALSE
## 539 0xx000 0.193163496 p254|p271 2 FALSE
## 540 0xxx00 0.046620555 p254|p271|p272 3 FALSE
## 541 0xxxx0 0.121176931 p254|p271|p272|p306 4 FALSE
## 542 0xxxxx -0.371011266 p254|p271|p272|p306|p313 5 FALSE
## 543 0xxx0x 0.245998107 p254|p271|p272|p313 4 FALSE
## 544 0xx0x0 -0.104307857 p254|p271|p306 3 FALSE
## 545 0xx0xx -0.144065997 p254|p271|p306|p313 4 FALSE
## 546 0xx00x -0.074853779 p254|p271|p313 3 FALSE
## 547 0x0x00 0.172155890 p254|p272 2 FALSE
## 548 0x0xx0 0.396023444 p254|p272|p306 3 FALSE
## 549 0x0xxx -0.115850549 p254|p272|p306|p313 4 FALSE
## 550 0x0x0x -0.076823163 p254|p272|p313 3 FALSE
## 551 0x00xx 0.109757752 p254|p306|p313 3 FALSE
## 552 0x000x 0.201292139 p254|p313 2 FALSE
## 553 00x000 -0.193820026 p271 1 FALSE
## 554 00xx00 0.068881289 p271|p272 2 FALSE
## 555 00xxx0 0.084649825 p271|p272|p306 3 FALSE
## 556 00xxxx -0.325427254 p271|p272|p306|p313 4 FALSE
## 557 00xx0x -0.242765697 p271|p272|p313 3 FALSE
## 558 00x0x0 0.009392724 p271|p306 2 FALSE
## 559 00x0xx 1.058951252 p271|p306|p313 3 FALSE
## 560 00x00x 0.064822433 p271|p313 2 FALSE
## 561 000x00 0.274157849 p272 1 FALSE
## 562 000xx0 -0.326757677 p272|p306 2 FALSE
## 563 000xxx 0.357469266 p272|p306|p313 3 FALSE
## 564 000x0x 0.162411562 p272|p313 2 FALSE
## 565 0000xx -0.663372708 p306|p313 2 FALSE
## 566 00000x -0.065501549 p313 1 FALSE
## 567 00x00 0.178976947 p104 1 FALSE
## 568 00xx0 0.181515143 p104|p182 2 FALSE
## 569 00xxx -1.541219402 p104|p182|p238 3 FALSE
## 570 000x0 -0.090979146 p182 1 FALSE
## 571 000xx 1.450683405 p182|p238 2 FALSE
## 572 0x000 0.000000000 p42 1 FALSE
## 573 0xx00 1.022420177 p42|p104 2 FALSE
## 574 0xxx0 -1.621940056 p42|p104|p182 3 FALSE
## 575 0x0x0 1.292376270 p42|p182 2 FALSE
## 576 0x0xx -1.456997729 p42|p182|p238 3 FALSE
## 577 0x00x 1.215243383 p42|p238 2 FALSE
## 578 x0000 0.000000000 p4205 1 FALSE
## 579 x0x00 0.132776914 p4205|p104 2 FALSE
## 580 x0xx0 -0.181515143 p4205|p104|p182 3 FALSE
## 581 x0xxx -0.102432189 p4205|p104|p182|p238 4 FALSE
## 582 x0x0x -0.132776914 p4205|p104|p238 3 FALSE
## 583 x00x0 0.090979146 p4205|p182 2 FALSE
## 584 x00xx 0.959642779 p4205|p182|p238 3 FALSE
## 585 x000x 0.000000000 p4205|p238 2 FALSE
## 586 xx000 0.000000000 p4205|p42 2 FALSE
## 587 xxx00 0.036893824 p4205|p42|p104 3 FALSE
## 588 xxxx0 1.452269318 p4205|p42|p104|p182 4 FALSE
## 589 xxx0x -1.324817217 p4205|p42|p104|p238 4 FALSE
## 590 xx0x0 -1.292376270 p4205|p42|p182 3 FALSE
## 591 xx0xx -0.953328455 p4205|p42|p182|p238 4 FALSE
## 592 xx00x 1.195082801 p4205|p42|p238 3 FALSE
## unique_id enzyme_name measure_type cond means
## 1 DHFR_ic75_palmer DHFR ic75 palmer 1.2062354930
## 2 DHFR_ic75_palmer DHFR ic75 palmer 0.8239107693
## 3 DHFR_ic75_palmer DHFR ic75 palmer 0.2172161842
## 4 DHFR_ic75_palmer DHFR ic75 palmer 0.0869399897
## 5 DHFR_ic75_palmer DHFR ic75 palmer -3.3818358819
## 6 DHFR_ic75_palmer DHFR ic75 palmer -7.1891355892
## 7 DHFR_ic75_palmer DHFR ic75 palmer 6.8412959859
## 8 DHFR_ic75_palmer DHFR ic75 palmer -4.2235981639
## 9 DHFR_ic75_palmer DHFR ic75 palmer -1.0190350137
## 10 DHFR_ic75_palmer DHFR ic75 palmer -0.5944141171
## 11 DHFR_ic75_palmer DHFR ic75 palmer 0.0104543165
## 12 DHFR_ic75_palmer DHFR ic75 palmer 1.8693802891
## 13 DHFR_ic75_palmer DHFR ic75 palmer -0.4655815508
## 14 DHFR_ic75_palmer DHFR ic75 palmer -0.2289452136
## 15 DHFR_ic75_palmer DHFR ic75 palmer 0.0443594841
## 16 DHFR_ic75_palmer DHFR ic75 palmer 2.4248366377
## 17 DHFR_ic75_palmer DHFR ic75 palmer -0.3734793838
## 18 DHFR_ic75_palmer DHFR ic75 palmer 0.2155007877
## 19 DHFR_ic75_palmer DHFR ic75 palmer -1.1774922053
## 20 DHFR_ic75_palmer DHFR ic75 palmer -3.0809917088
## 21 DHFR_ic75_palmer DHFR ic75 palmer 2.5732055642
## 22 DHFR_ic75_palmer DHFR ic75 palmer -2.3319717766
## 23 DHFR_ic75_palmer DHFR ic75 palmer -0.0456122478
## 24 DHFR_ic75_palmer DHFR ic75 palmer 0.1027002613
## 25 DHFR_ic75_palmer DHFR ic75 palmer 0.1307275253
## 26 DHFR_ic75_palmer DHFR ic75 palmer 1.9214364729
## 27 DHFR_ic75_palmer DHFR ic75 palmer 2.2547035770
## 28 DHFR_ic75_palmer DHFR ic75 palmer 1.6478415588
## 29 DHFR_ic75_palmer DHFR ic75 palmer 0.6951950425
## 30 DHFR_ic75_palmer DHFR ic75 palmer 1.7901616616
## 31 DHFR_ic75_palmer DHFR ic75 palmer 0.1627485133
## 32 DHFR_ic75_palmer DHFR ic75 palmer -0.6411418079
## 33 DHFR_ic75_palmer DHFR ic75 palmer 0.5351546470
## 34 DHFR_ic75_palmer DHFR ic75 palmer 1.1245241043
## 35 DHFR_ic75_palmer DHFR ic75 palmer -1.3228789457
## 36 DHFR_ic75_palmer DHFR ic75 palmer -2.6399845605
## 37 DHFR_ic75_palmer DHFR ic75 palmer -0.8278135882
## 38 DHFR_ic75_palmer DHFR ic75 palmer 0.9472975263
## 39 DHFR_ic75_palmer DHFR ic75 palmer -1.8621430146
## 40 DHFR_ic75_palmer DHFR ic75 palmer 1.6952992211
## 41 DHFR_ic75_palmer DHFR ic75 palmer -0.9917867991
## 42 DHFR_ic75_palmer DHFR ic75 palmer 0.7602580277
## 43 DHFR_ic75_palmer DHFR ic75 palmer 2.8704376498
## 44 DHFR_ic75_palmer DHFR ic75 palmer -0.8496609691
## 45 DHFR_ic75_palmer DHFR ic75 palmer 0.2988833330
## 46 DHFR_ic75_palmer DHFR ic75 palmer 0.2794516133
## 47 DHFR_ic75_palmer DHFR ic75 palmer -0.6590651019
## 48 DHFR_ic75_palmer DHFR ic75 palmer 0.4601334499
## 49 DHFR_ic75_palmer DHFR ic75 palmer -0.8931744546
## 50 DHFR_ic75_palmer DHFR ic75 palmer -0.9737236775
## 51 DHFR_ic75_palmer DHFR ic75 palmer -0.0430876032
## 52 DHFR_ic75_palmer DHFR ic75 palmer 0.3528148943
## 53 DHFR_ic75_palmer DHFR ic75 palmer -1.0887019893
## 54 DHFR_ic75_palmer DHFR ic75 palmer 0.4966773128
## 55 DHFR_ic75_palmer DHFR ic75 palmer -0.6911103475
## 56 DHFR_ic75_palmer DHFR ic75 palmer 0.6800591798
## 57 DHFR_ic75_palmer DHFR ic75 palmer -1.8036554355
## 58 DHFR_ic75_palmer DHFR ic75 palmer 0.1999543556
## 59 DHFR_ic75_palmer DHFR ic75 palmer -0.5632148850
## 60 DHFR_ic75_palmer DHFR ic75 palmer -0.1737197244
## 61 DHFR_ki_trajg DHFR ki trajg 0.9697491865
## 62 DHFR_ki_trajg DHFR ki trajg 0.4109127529
## 63 DHFR_ki_trajg DHFR ki trajg 0.0820066602
## 64 DHFR_ki_trajg DHFR ki trajg -0.3698938721
## 65 DHFR_ki_trajg DHFR ki trajg -0.3746933214
## 66 DHFR_ki_trajg DHFR ki trajg -0.1078647001
## 67 DHFR_ki_trajg DHFR ki trajg 0.3260605832
## 68 DHFR_ki_trajg DHFR ki trajg 0.0776194738
## 69 DHFR_ki_trajg DHFR ki trajg 0.0773038378
## 70 DHFR_ki_trajg DHFR ki trajg 0.4387885429
## 71 DHFR_ki_trajg DHFR ki trajg 1.6725909180
## 72 DHFR_ki_trajg DHFR ki trajg -0.2379599604
## 73 DHFR_ki_trajg DHFR ki trajg 0.2140346062
## 74 DHFR_ki_trajg DHFR ki trajg -0.5594039435
## 75 DHFR_ki_trajg DHFR ki trajg 0.0156588163
## 76 DHFR_ki_trajg DHFR ki trajg 1.1162865010
## 77 DHFR_ki_trajg DHFR ki trajg 0.4024060198
## 78 DHFR_ki_trajg DHFR ki trajg -0.1485079546
## 79 DHFR_ki_trajg DHFR ki trajg 0.4189568454
## 80 DHFR_ki_trajg DHFR ki trajg -0.0180350968
## 81 DHFR_ki_trajg DHFR ki trajg 0.2569441197
## 82 DHFR_ki_trajg DHFR ki trajg -0.2775070636
## 83 DHFR_ki_trajg DHFR ki trajg -0.7159940330
## 84 DHFR_ki_trajg DHFR ki trajg 1.3184002269
## 85 DHFR_ki_trajg DHFR ki trajg 0.5356077160
## 86 DHFR_ki_trajg DHFR ki trajg 1.4946366750
## 87 DHFR_ki_trajr DHFR ki trajr 0.9837809276
## 88 DHFR_ki_trajr DHFR ki trajr 0.2391675950
## 89 DHFR_ki_trajr DHFR ki trajr 0.4455707916
## 90 DHFR_ki_trajr DHFR ki trajr 0.3572343908
## 91 DHFR_ki_trajr DHFR ki trajr -0.4513550160
## 92 DHFR_ki_trajr DHFR ki trajr 0.1502588862
## 93 DHFR_ki_trajr DHFR ki trajr -0.0102813748
## 94 DHFR_ki_trajr DHFR ki trajr -0.1460729622
## 95 DHFR_ki_trajr DHFR ki trajr -0.0079650572
## 96 DHFR_ki_trajr DHFR ki trajr 1.0204882392
## 97 DHFR_ki_trajr DHFR ki trajr -0.5640112998
## 98 DHFR_ki_trajr DHFR ki trajr 0.2420980885
## 99 DHFR_ki_trajr DHFR ki trajr -0.0307196048
## 100 DHFR_ki_trajr DHFR ki trajr 0.2800009857
## 101 DHFR_ki_trajr DHFR ki trajr 0.9702638982
## 102 DHFR_ki_trajr DHFR ki trajr 0.0922497590
## 103 DHFR_ki_trajr DHFR ki trajr -0.7688204762
## 104 DHFR_ki_trajr DHFR ki trajr -0.6872644817
## 105 DHFR_ki_trajr DHFR ki trajr 0.1269116399
## 106 DHFR_ki_trajr DHFR ki trajr 0.4075661730
## 107 DHFR_ki_trajr DHFR ki trajr 0.4697447546
## 108 DHFR_ki_trajr DHFR ki trajr 0.0485582393
## 109 DHFR_ki_trajr DHFR ki trajr -0.2779476645
## 110 DHFR_ki_trajr DHFR ki trajr 0.9680966144
## 111 DHFR_ki_trajr DHFR ki trajr -0.0381671545
## 112 DHFR_ki_trajr DHFR ki trajr 1.2077492397
## 113 MPH_catact_ZnPTM MPH catact ZnPTM 0.6014530846
## 114 MPH_catact_ZnPTM MPH catact ZnPTM -0.0262381099
## 115 MPH_catact_ZnPTM MPH catact ZnPTM 0.0298359555
## 116 MPH_catact_ZnPTM MPH catact ZnPTM 0.0829901928
## 117 MPH_catact_ZnPTM MPH catact ZnPTM -0.1933522503
## 118 MPH_catact_ZnPTM MPH catact ZnPTM 0.6141302903
## 119 MPH_catact_ZnPTM MPH catact ZnPTM 0.5759713003
## 120 MPH_catact_ZnPTM MPH catact ZnPTM 0.5753711677
## 121 MPH_catact_ZnPTM MPH catact ZnPTM -0.0779951552
## 122 MPH_catact_ZnPTM MPH catact ZnPTM -0.4725758154
## 123 MPH_catact_ZnPTM MPH catact ZnPTM 0.6166938011
## 124 MPH_catact_ZnPTM MPH catact ZnPTM 0.6761999762
## 125 MPH_catact_ZnPTM MPH catact ZnPTM 0.5888533443
## 126 MPH_catact_ZnPTM MPH catact ZnPTM 0.7147608105
## 127 MPH_catact_ZnPTM MPH catact ZnPTM -0.0329280268
## 128 MPH_catact_ZnPTM MPH catact ZnPTM -0.1735958962
## 129 MPH_catact_ZnPTM MPH catact ZnPTM 0.2389949571
## 130 MPH_catact_ZnPTM MPH catact ZnPTM -0.0179745831
## 131 MPH_catact_ZnPTM MPH catact ZnPTM 1.3320209103
## 132 MPH_catact_ZnPTM MPH catact ZnPTM -0.5190551772
## 133 MPH_catact_ZnPTM MPH catact ZnPTM 0.0547379301
## 134 MPH_catact_ZnPTM MPH catact ZnPTM 0.0780273638
## 135 MPH_catact_ZnPTM MPH catact ZnPTM 0.6575819217
## 136 MPH_catact_ZnPTM MPH catact ZnPTM 0.1822762261
## 137 MPH_catact_ZnPTM MPH catact ZnPTM 1.1382346025
## 138 MPH_catact_ZnPTM MPH catact ZnPTM 0.6027861091
## 139 NfsA_ec50_2039 NfsA ec50 2039 -0.1836051261
## 140 NfsA_ec50_2039 NfsA ec50 2039 0.0681046216
## 141 NfsA_ec50_2039 NfsA ec50 2039 -0.0186503117
## 142 NfsA_ec50_2039 NfsA ec50 2039 -0.0062579777
## 143 NfsA_ec50_2039 NfsA ec50 2039 0.0916418075
## 144 NfsA_ec50_2039 NfsA ec50 2039 -0.1062233107
## 145 NfsA_ec50_2039 NfsA ec50 2039 0.0752331625
## 146 NfsA_ec50_2039 NfsA ec50 2039 -0.0067424598
## 147 NfsA_ec50_2039 NfsA ec50 2039 -0.0365279925
## 148 NfsA_ec50_2039 NfsA ec50 2039 0.0386176943
## 149 NfsA_ec50_2039 NfsA ec50 2039 -0.0364612571
## 150 NfsA_ec50_2039 NfsA ec50 2039 -0.1043226875
## 151 NfsA_ec50_2039 NfsA ec50 2039 0.2455043396
## 152 NfsA_ec50_2039 NfsA ec50 2039 -0.0039551835
## 153 NfsA_ec50_2039 NfsA ec50 2039 0.3517777861
## 154 NfsA_ec50_2039 NfsA ec50 2039 0.0709342958
## 155 NfsA_ec50_2039 NfsA ec50 2039 -0.0105120513
## 156 NfsA_ec50_2039 NfsA ec50 2039 -0.0298045492
## 157 NfsA_ec50_2039 NfsA ec50 2039 -0.0255586573
## 158 NfsA_ec50_2039 NfsA ec50 2039 0.0695594275
## 159 NfsA_ec50_2039 NfsA ec50 2039 0.0428042028
## 160 NfsA_ec50_2039 NfsA ec50 2039 0.0337737283
## 161 NfsA_ec50_2039 NfsA ec50 2039 -0.0359432925
## 162 NfsA_ec50_2039 NfsA ec50 2039 -0.0903033454
## 163 NfsA_ec50_2039 NfsA ec50 2039 -0.0556827046
## 164 NfsA_ec50_2039 NfsA ec50 2039 -0.0281368788
## 165 NfsA_ec50_2039 NfsA ec50 2039 -0.0177819590
## 166 NfsA_ec50_2039 NfsA ec50 2039 -0.1324799126
## 167 NfsA_ec50_2039 NfsA ec50 2039 -0.0358644364
## 168 NfsA_ec50_2039 NfsA ec50 2039 -0.2410580287
## 169 NfsA_ec50_2039 NfsA ec50 2039 0.1397653277
## 170 NfsA_ec50_2039 NfsA ec50 2039 0.0963359828
## 171 NfsA_ec50_2039 NfsA ec50 2039 0.1058026609
## 172 NfsA_ec50_2039 NfsA ec50 2039 0.0075018912
## 173 NfsA_ec50_2039 NfsA ec50 2039 -0.0220382404
## 174 NfsA_ec50_2039 NfsA ec50 2039 0.0328627091
## 175 NfsA_ec50_2039 NfsA ec50 2039 0.0912835621
## 176 NfsA_ec50_2039 NfsA ec50 2039 -0.0629425668
## 177 NfsA_ec50_2039 NfsA ec50 2039 0.0346434105
## 178 NfsA_ec50_2039 NfsA ec50 2039 0.0975715483
## 179 NfsA_ec50_2039 NfsA ec50 2039 -0.0173590203
## 180 NfsA_ec50_2039 NfsA ec50 2039 0.0653162028
## 181 NfsA_ec50_2039 NfsA ec50 2039 -0.1337930941
## 182 NfsA_ec50_2039 NfsA ec50 2039 0.2123040146
## 183 NfsA_ec50_2039 NfsA ec50 2039 0.0339294274
## 184 NfsA_ec50_2039 NfsA ec50 2039 0.2326842483
## 185 NfsA_ec50_2039 NfsA ec50 2039 0.0906688737
## 186 NfsA_ec50_2039 NfsA ec50 2039 -0.0193446645
## 187 NfsA_ec50_2039 NfsA ec50 2039 -0.0274577404
## 188 NfsA_ec50_2039 NfsA ec50 2039 -0.0044199260
## 189 NfsA_ec50_2039 NfsA ec50 2039 0.0062850048
## 190 NfsA_ec50_2039 NfsA ec50 2039 -0.0280033491
## 191 NfsA_ec50_2039 NfsA ec50 2039 -0.0146378985
## 192 NfsA_ec50_2039 NfsA ec50 2039 -0.0229229906
## 193 NfsA_ec50_2039 NfsA ec50 2039 -0.0166336540
## 194 NfsA_ec50_2039 NfsA ec50 2039 0.0163758077
## 195 NfsA_ec50_2039 NfsA ec50 2039 -0.0629292093
## 196 NfsA_ec50_2039 NfsA ec50 2039 -0.0978057282
## 197 NfsA_ec50_2039 NfsA ec50 2039 -0.1077205690
## 198 NfsA_ec50_2039 NfsA ec50 2039 0.0521695212
## 199 NfsA_ec50_2039 NfsA ec50 2039 0.2889894811
## 200 NfsA_ec50_2039 NfsA ec50 2039 0.1275384125
## 201 NfsA_ec50_2039 NfsA ec50 2039 0.3094435780
## 202 NfsA_ec50_2039 NfsA ec50 2039 0.3579534467
## 203 NfsA_ec50_2039 NfsA ec50 2039 -0.1455269649
## 204 NfsA_ec50_2039 NfsA ec50 2039 0.0204132770
## 205 NfsA_ec50_2039 NfsA ec50 2039 -0.0004683640
## 206 NfsA_ec50_2039 NfsA ec50 2039 0.0200263949
## 207 NfsA_ec50_2039 NfsA ec50 2039 0.2315052064
## 208 NfsA_ec50_2039 NfsA ec50 2039 0.3065119830
## 209 NfsA_ec50_2039 NfsA ec50 2039 0.0355119615
## 210 NfsA_ec50_2039 NfsA ec50 2039 0.1239119618
## 211 NfsA_ec50_2039 NfsA ec50 2039 0.1770238631
## 212 NfsA_ec50_2039 NfsA ec50 2039 -0.0211872665
## 213 NfsA_ec50_2039 NfsA ec50 2039 0.2825320265
## 214 NfsA_ec50_2039 NfsA ec50 2039 -0.3709280451
## 215 NfsA_ec50_2039 NfsA ec50 2039 -0.0382235176
## 216 NfsA_ec50_2039 NfsA ec50 2039 -0.0089850783
## 217 NfsA_ec50_2039 NfsA ec50 2039 -0.1245892565
## 218 NfsA_ec50_2039 NfsA ec50 2039 0.0745672884
## 219 NfsA_ec50_2039 NfsA ec50 2039 -0.0478116786
## 220 NfsA_ec50_2039 NfsA ec50 2039 0.0303642996
## 221 NfsA_ec50_2039 NfsA ec50 2039 -0.2624441094
## 222 NfsA_ec50_2039 NfsA ec50 2039 -0.0812688215
## 223 NfsA_ec50_2039 NfsA ec50 2039 0.0529152655
## 224 NfsA_ec50_2039 NfsA ec50 2039 0.2146652139
## 225 NfsA_ec50_2039 NfsA ec50 2039 0.0783215107
## 226 NfsA_ec50_2039 NfsA ec50 2039 0.0891218831
## 227 NfsA_ec50_2039 NfsA ec50 2039 -0.0297717435
## 228 NfsA_ec50_2039 NfsA ec50 2039 0.0468898238
## 229 NfsA_ec50_2039 NfsA ec50 2039 -0.0227298126
## 230 NfsA_ec50_2039 NfsA ec50 2039 0.5286519492
## 231 NfsA_ec50_2039 NfsA ec50 2039 -0.0403094169
## 232 NfsA_ec50_2039 NfsA ec50 2039 -0.1051249211
## 233 NfsA_ec50_2039 NfsA ec50 2039 0.0073342133
## 234 NfsA_ec50_2039 NfsA ec50 2039 -0.0252371270
## 235 NfsA_ec50_2039 NfsA ec50 2039 -0.0236105839
## 236 NfsA_ec50_2039 NfsA ec50 2039 0.1727504818
## 237 NfsA_ec50_2039 NfsA ec50 2039 -0.0086689450
## 238 NfsA_ec50_2039 NfsA ec50 2039 0.1369784273
## 239 NfsA_ec50_2039 NfsA ec50 2039 0.0017264238
## 240 NfsA_ec50_2039 NfsA ec50 2039 0.0432167804
## 241 NfsA_ec50_2039 NfsA ec50 2039 0.0005318277
## 242 NfsA_ec50_2039 NfsA ec50 2039 0.0516320608
## 243 NfsA_ec50_2039 NfsA ec50 2039 0.0512407834
## 244 NfsA_ec50_2039 NfsA ec50 2039 -0.0392870444
## 245 NfsA_ec50_2039 NfsA ec50 2039 0.0058148649
## 246 NfsA_ec50_2039 NfsA ec50 2039 0.0040635316
## 247 NfsA_ec50_2039 NfsA ec50 2039 -0.0651786412
## 248 NfsA_ec50_2039 NfsA ec50 2039 0.0377675481
## 249 NfsA_ec50_2039 NfsA ec50 2039 0.0357611152
## 250 NfsA_ec50_2039 NfsA ec50 2039 0.0795724700
## 251 NfsA_ec50_2039 NfsA ec50 2039 0.0143172459
## 252 NfsA_ec50_2039 NfsA ec50 2039 -0.0049343146
## 253 NfsA_ec50_2039 NfsA ec50 2039 0.0165982946
## 254 NfsA_ec50_2039 NfsA ec50 2039 0.0251150682
## 255 NfsA_ec50_2039 NfsA ec50 2039 0.0117068309
## 256 NfsA_ec50_2039 NfsA ec50 2039 0.0584999147
## 257 NfsA_ec50_2039 NfsA ec50 2039 0.1708262589
## 258 NfsA_ec50_2039 NfsA ec50 2039 -0.0262790812
## 259 NfsA_ec50_2039 NfsA ec50 2039 -0.0353591048
## 260 NfsA_ec50_2039 NfsA ec50 2039 -0.1138374545
## 261 NfsA_ec50_3637 NfsA ec50 3637 -0.0569706652
## 262 NfsA_ec50_3637 NfsA ec50 3637 0.0207202924
## 263 NfsA_ec50_3637 NfsA ec50 3637 0.0617297495
## 264 NfsA_ec50_3637 NfsA ec50 3637 -0.1321747554
## 265 NfsA_ec50_3637 NfsA ec50 3637 0.2251203280
## 266 NfsA_ec50_3637 NfsA ec50 3637 0.9181359551
## 267 NfsA_ec50_3637 NfsA ec50 3637 -0.2032811849
## 268 NfsA_ec50_3637 NfsA ec50 3637 -0.0733541727
## 269 NfsA_ec50_3637 NfsA ec50 3637 -0.3954314003
## 270 NfsA_ec50_3637 NfsA ec50 3637 0.1603000840
## 271 NfsA_ec50_3637 NfsA ec50 3637 0.0692931523
## 272 NfsA_ec50_3637 NfsA ec50 3637 0.0172715963
## 273 NfsA_ec50_3637 NfsA ec50 3637 -0.2790421770
## 274 NfsA_ec50_3637 NfsA ec50 3637 0.0926146264
## 275 NfsA_ec50_3637 NfsA ec50 3637 0.2187172032
## 276 NfsA_ec50_3637 NfsA ec50 3637 -0.2894908905
## 277 NfsA_ec50_3637 NfsA ec50 3637 -0.1314996851
## 278 NfsA_ec50_3637 NfsA ec50 3637 -0.0734456760
## 279 NfsA_ec50_3637 NfsA ec50 3637 -0.0248115637
## 280 NfsA_ec50_3637 NfsA ec50 3637 -0.2871770486
## 281 NfsA_ec50_3637 NfsA ec50 3637 -0.1946197462
## 282 NfsA_ec50_3637 NfsA ec50 3637 0.0122059190
## 283 NfsA_ec50_3637 NfsA ec50 3637 0.0057623657
## 284 NfsA_ec50_3637 NfsA ec50 3637 0.0236221014
## 285 NfsA_ec50_3637 NfsA ec50 3637 -0.0741606229
## 286 NfsA_ec50_3637 NfsA ec50 3637 0.0225808245
## 287 NfsA_ec50_3637 NfsA ec50 3637 0.1432529308
## 288 NfsA_ec50_3637 NfsA ec50 3637 0.1044974531
## 289 NfsA_ec50_3637 NfsA ec50 3637 -0.1794361674
## 290 NfsA_ec50_3637 NfsA ec50 3637 -0.1712352281
## 291 NfsA_ec50_3637 NfsA ec50 3637 0.1681112032
## 292 NfsA_ec50_3637 NfsA ec50 3637 0.1876979101
## 293 NfsA_ec50_3637 NfsA ec50 3637 0.1242066126
## 294 NfsA_ec50_3637 NfsA ec50 3637 0.0060707290
## 295 NfsA_ec50_3637 NfsA ec50 3637 0.0273060835
## 296 NfsA_ec50_3637 NfsA ec50 3637 -0.0580679126
## 297 NfsA_ec50_3637 NfsA ec50 3637 -0.1621399356
## 298 NfsA_ec50_3637 NfsA ec50 3637 -0.0461866784
## 299 NfsA_ec50_3637 NfsA ec50 3637 0.0826201157
## 300 NfsA_ec50_3637 NfsA ec50 3637 0.0611866980
## 301 NfsA_ec50_3637 NfsA ec50 3637 -0.0234087043
## 302 NfsA_ec50_3637 NfsA ec50 3637 0.0748541626
## 303 NfsA_ec50_3637 NfsA ec50 3637 0.0553774954
## 304 NfsA_ec50_3637 NfsA ec50 3637 0.0922134343
## 305 NfsA_ec50_3637 NfsA ec50 3637 -0.0564313255
## 306 NfsA_ec50_3637 NfsA ec50 3637 0.0927015930
## 307 NfsA_ec50_3637 NfsA ec50 3637 -0.1914053813
## 308 NfsA_ec50_3637 NfsA ec50 3637 0.0034482452
## 309 NfsA_ec50_3637 NfsA ec50 3637 0.0743519850
## 310 NfsA_ec50_3637 NfsA ec50 3637 -0.0222180181
## 311 NfsA_ec50_3637 NfsA ec50 3637 0.1118174567
## 312 NfsA_ec50_3637 NfsA ec50 3637 0.1675711646
## 313 NfsA_ec50_3637 NfsA ec50 3637 -0.0544554698
## 314 NfsA_ec50_3637 NfsA ec50 3637 -0.0782406699
## 315 NfsA_ec50_3637 NfsA ec50 3637 -0.1210110456
## 316 NfsA_ec50_3637 NfsA ec50 3637 0.0633115530
## 317 NfsA_ec50_3637 NfsA ec50 3637 -0.0562297539
## 318 NfsA_ec50_3637 NfsA ec50 3637 0.3126121933
## 319 NfsA_ec50_3637 NfsA ec50 3637 -0.0750305493
## 320 NfsA_ec50_3637 NfsA ec50 3637 -0.0653991572
## 321 NfsA_ec50_3637 NfsA ec50 3637 0.0523536890
## 322 NfsA_ec50_3637 NfsA ec50 3637 0.1660586859
## 323 NfsA_ec50_3637 NfsA ec50 3637 0.2481057780
## 324 NfsA_ec50_3637 NfsA ec50 3637 0.2334684124
## 325 NfsA_ec50_3637 NfsA ec50 3637 -0.0902993257
## 326 NfsA_ec50_3637 NfsA ec50 3637 0.1100160218
## 327 NfsA_ec50_3637 NfsA ec50 3637 -0.4441508278
## 328 NfsA_ec50_3637 NfsA ec50 3637 0.1480407335
## 329 NfsA_ec50_3637 NfsA ec50 3637 0.0349365724
## 330 NfsA_ec50_3637 NfsA ec50 3637 -0.1825827093
## 331 NfsA_ec50_3637 NfsA ec50 3637 -0.1683354659
## 332 NfsA_ec50_3637 NfsA ec50 3637 0.2066348605
## 333 NfsA_ec50_3637 NfsA ec50 3637 0.1466547909
## 334 NfsA_ec50_3637 NfsA ec50 3637 -0.0751033646
## 335 NfsA_ec50_3637 NfsA ec50 3637 0.2265367060
## 336 NfsA_ec50_3637 NfsA ec50 3637 -0.2405484081
## 337 NfsA_ec50_3637 NfsA ec50 3637 -0.0482498806
## 338 NfsA_ec50_3637 NfsA ec50 3637 0.1296145456
## 339 NfsA_ec50_3637 NfsA ec50 3637 0.1452661161
## 340 NfsA_ec50_3637 NfsA ec50 3637 0.0166145089
## 341 NfsA_ec50_3637 NfsA ec50 3637 -0.1953665691
## 342 NfsA_ec50_3637 NfsA ec50 3637 0.5683588741
## 343 NfsA_ec50_3637 NfsA ec50 3637 0.2764296357
## 344 NfsA_ec50_3637 NfsA ec50 3637 0.1256842504
## 345 NfsA_ec50_3637 NfsA ec50 3637 0.4798621280
## 346 NfsA_ec50_3637 NfsA ec50 3637 0.1207426029
## 347 NfsA_ec50_3637 NfsA ec50 3637 -0.1216801227
## 348 NfsA_ec50_3637 NfsA ec50 3637 -0.0055669317
## 349 NfsA_ec50_3637 NfsA ec50 3637 0.3247082162
## 350 NfsA_ec50_3637 NfsA ec50 3637 -0.1909947989
## 351 NfsA_ec50_3637 NfsA ec50 3637 0.2095135629
## 352 NfsA_ec50_3637 NfsA ec50 3637 -0.4965798123
## 353 NfsA_ec50_3637 NfsA ec50 3637 -0.1818802618
## 354 NfsA_ec50_3637 NfsA ec50 3637 -0.0123674545
## 355 NfsA_ec50_3637 NfsA ec50 3637 -0.0199667765
## 356 NfsA_ec50_3637 NfsA ec50 3637 0.1093012756
## 357 NfsA_ec50_3637 NfsA ec50 3637 -0.1985136418
## 358 NfsA_ec50_3637 NfsA ec50 3637 -0.4374371314
## 359 NfsA_ec50_3637 NfsA ec50 3637 0.0334701705
## 360 NfsA_ec50_3637 NfsA ec50 3637 0.1032024493
## 361 NfsA_ec50_3637 NfsA ec50 3637 -0.1092382943
## 362 NfsA_ec50_3637 NfsA ec50 3637 -0.0813533852
## 363 NfsA_ec50_3637 NfsA ec50 3637 -0.1803026806
## 364 NfsA_ec50_3637 NfsA ec50 3637 -0.0089813811
## 365 NfsA_ec50_3637 NfsA ec50 3637 -0.0887530639
## 366 NfsA_ec50_3637 NfsA ec50 3637 0.2248910047
## 367 NfsA_ec50_3637 NfsA ec50 3637 0.0124182728
## 368 NfsA_ec50_3637 NfsA ec50 3637 -0.1179390815
## 369 NfsA_ec50_3637 NfsA ec50 3637 0.2463504336
## 370 NfsA_ec50_3637 NfsA ec50 3637 -0.0788818339
## 371 NfsA_ec50_3637 NfsA ec50 3637 -0.0385841910
## 372 NfsA_ec50_3637 NfsA ec50 3637 0.0355885181
## 373 NfsA_ec50_3637 NfsA ec50 3637 -0.2867550554
## 374 NfsA_ec50_3637 NfsA ec50 3637 -0.0555546182
## 375 NfsA_ec50_3637 NfsA ec50 3637 -0.0906614568
## 376 NfsA_ec50_3637 NfsA ec50 3637 0.0375301472
## 377 NfsA_ec50_3637 NfsA ec50 3637 0.0669668060
## 378 NfsA_ec50_3637 NfsA ec50 3637 0.1206408259
## 379 NfsA_ec50_3637 NfsA ec50 3637 -0.3608554723
## 380 NfsA_ec50_3637 NfsA ec50 3637 -0.0562624733
## 381 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.3320456373
## 382 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 -0.0018436095
## 383 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.3308587296
## 384 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.0257601887
## 385 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 -0.0309832222
## 386 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.1279913659
## 387 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 -0.0362998491
## 388 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.0051600705
## 389 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.2345885840
## 390 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.0021184273
## 391 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.4520073760
## 392 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.5072447682
## 393 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.2322662635
## 394 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.2011161233
## 395 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.3675270797
## 396 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0776672023
## 397 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1469379670
## 398 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1729186185
## 399 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1034867917
## 400 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0245774437
## 401 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0962601272
## 402 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0374030371
## 403 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.3571733510
## 404 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1761771660
## 405 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.2310929181
## 406 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1550315534
## 407 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.0836030575
## 408 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.2079829987
## 409 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0636474609
## 410 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1537873915
## 411 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.3862382251
## 412 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1370188947
## 413 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1334705379
## 414 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0620513175
## 415 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0826698657
## 416 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0804127445
## 417 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1021430941
## 418 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0291861163
## 419 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1708379190
## 420 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1726222318
## 421 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0526166129
## 422 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.0839710533
## 423 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.0835174321
## 424 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0177659322
## 425 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.0023086958
## 426 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1701052593
## 427 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.2339087945
## 428 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1575969765
## 429 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1286436265
## 430 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.2046346501
## 431 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1116113314
## 432 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.3103155767
## 433 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1005210644
## 434 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1105589638
## 435 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.2858996573
## 436 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.2687554330
## 437 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0179728892
## 438 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1203778663
## 439 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.2929293612
## 440 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1523968960
## 441 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1963371276
## 442 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1482655882
## 443 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1611956215
## 444 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.1346938665
## 445 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.2240176489
## 446 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.0151834977
## 447 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.0503255758
## 448 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.1977983004
## 449 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0610598579
## 450 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0409902741
## 451 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0960047615
## 452 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0319766797
## 453 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.2597174797
## 454 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.1646899644
## 455 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0126269844
## 456 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0418167864
## 457 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.1029315531
## 458 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0897087574
## 459 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0399018082
## 460 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0179960429
## 461 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0975201602
## 462 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0398744580
## 463 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0192487103
## 464 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0011875442
## 465 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0520545989
## 466 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.1274031534
## 467 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0016661053
## 468 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0788291217
## 469 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.1658703587
## 470 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0128113183
## 471 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0273208218
## 472 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0032190631
## 473 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0337077327
## 474 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0415368472
## 475 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.1253463593
## 476 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0134890705
## 477 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0119185296
## 478 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0273295870
## 479 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.3584109834
## 480 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0458425116
## 481 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.1030318338
## 482 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0849993482
## 483 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.2143528103
## 484 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.4726135136
## 485 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.2156369908
## 486 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.1649320251
## 487 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.1671852596
## 488 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0842277227
## 489 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0864575198
## 490 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0255847522
## 491 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0684301233
## 492 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.2718614986
## 493 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0723157477
## 494 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0957845105
## 495 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.1378797455
## 496 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.1277320188
## 497 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.3382977306
## 498 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0046617010
## 499 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0642127088
## 500 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.1439535601
## 501 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0021469899
## 502 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0106092530
## 503 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0314345394
## 504 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.1211614184
## 505 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0413610788
## 506 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.0566767551
## 507 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.0922228366
## 508 PTE_catact_2NH PTE catact 2NH 0.7285390388
## 509 PTE_catact_2NH PTE catact 2NH 0.6373364226
## 510 PTE_catact_2NH PTE catact 2NH -0.2450705571
## 511 PTE_catact_2NH PTE catact 2NH -0.4956750948
## 512 PTE_catact_2NH PTE catact 2NH -0.6668673267
## 513 PTE_catact_2NH PTE catact 2NH 0.2725240242
## 514 PTE_catact_2NH PTE catact 2NH -0.5812863605
## 515 PTE_catact_2NH PTE catact 2NH -1.6213040114
## 516 PTE_catact_2NH PTE catact 2NH -0.2268754483
## 517 PTE_catact_2NH PTE catact 2NH -0.0235867814
## 518 PTE_catact_2NH PTE catact 2NH -0.2614621340
## 519 PTE_catact_2NH PTE catact 2NH 0.6199565695
## 520 PTE_catact_2NH PTE catact 2NH 0.3578151577
## 521 PTE_catact_2NH PTE catact 2NH 0.0947106215
## 522 PTE_catact_2NH PTE catact 2NH -0.0564936082
## 523 PTE_catact_2NH PTE catact 2NH 0.0769863183
## 524 PTE_catact_2NH PTE catact 2NH 0.0904603121
## 525 PTE_catact_2NH PTE catact 2NH 0.2717675785
## 526 PTE_catact_2NH PTE catact 2NH 0.8347845561
## 527 PTE_catact_2NH PTE catact 2NH 0.3473048566
## 528 PTE_catact_2NH PTE catact 2NH -0.1365751694
## 529 PTE_catact_2NH PTE catact 2NH -0.5099230009
## 530 PTE_catact_2NH PTE catact 2NH 0.2087296979
## 531 PTE_catact_2NH PTE catact 2NH -0.4068882602
## 532 PTE_catact_2NH PTE catact 2NH -0.2946447369
## 533 PTE_catact_2NH PTE catact 2NH -0.2780698745
## 534 PTE_catact_2NH PTE catact 2NH -0.1593031117
## 535 PTE_catact_2NH PTE catact 2NH -0.5955769994
## 536 PTE_catact_2NH PTE catact 2NH -0.0596301158
## 537 PTE_catact_2NH PTE catact 2NH -0.1570006989
## 538 PTE_catact_2NH PTE catact 2NH 1.4715059467
## 539 PTE_catact_2NH PTE catact 2NH 0.0137585293
## 540 PTE_catact_2NH PTE catact 2NH -0.1103822900
## 541 PTE_catact_2NH PTE catact 2NH -0.3977623654
## 542 PTE_catact_2NH PTE catact 2NH -0.2347492538
## 543 PTE_catact_2NH PTE catact 2NH -0.2301507057
## 544 PTE_catact_2NH PTE catact 2NH -0.3752671809
## 545 PTE_catact_2NH PTE catact 2NH -1.1402236356
## 546 PTE_catact_2NH PTE catact 2NH -0.2300782640
## 547 PTE_catact_2NH PTE catact 2NH 0.3597276308
## 548 PTE_catact_2NH PTE catact 2NH 0.1752027516
## 549 PTE_catact_2NH PTE catact 2NH 0.0086221024
## 550 PTE_catact_2NH PTE catact 2NH 0.0744053780
## 551 PTE_catact_2NH PTE catact 2NH -0.0655980266
## 552 PTE_catact_2NH PTE catact 2NH 0.1022297216
## 553 PTE_catact_2NH PTE catact 2NH 0.1688986986
## 554 PTE_catact_2NH PTE catact 2NH 0.0224243247
## 555 PTE_catact_2NH PTE catact 2NH 0.0256556361
## 556 PTE_catact_2NH PTE catact 2NH -0.0935406092
## 557 PTE_catact_2NH PTE catact 2NH -0.2015806584
## 558 PTE_catact_2NH PTE catact 2NH 0.3272962613
## 559 PTE_catact_2NH PTE catact 2NH 0.4764903090
## 560 PTE_catact_2NH PTE catact 2NH 0.4376029757
## 561 PTE_catact_2NH PTE catact 2NH 0.2179017588
## 562 PTE_catact_2NH PTE catact 2NH -0.1814104041
## 563 PTE_catact_2NH PTE catact 2NH -0.0949573899
## 564 PTE_catact_2NH PTE catact 2NH 0.0065038763
## 565 PTE_catact_2NH PTE catact 2NH -0.1228109891
## 566 PTE_catact_2NH PTE catact 2NH -0.0440976935
## 567 TEM_MIC_weinreich TEM MIC weinreich 0.9959252876
## 568 TEM_MIC_weinreich TEM MIC weinreich -0.6380093140
## 569 TEM_MIC_weinreich TEM MIC weinreich -0.5617283740
## 570 TEM_MIC_weinreich TEM MIC weinreich 0.4404367458
## 571 TEM_MIC_weinreich TEM MIC weinreich 0.6828096293
## 572 TEM_MIC_weinreich TEM MIC weinreich 0.7668805522
## 573 TEM_MIC_weinreich TEM MIC weinreich 0.0416191642
## 574 TEM_MIC_weinreich TEM MIC weinreich 0.1349017256
## 575 TEM_MIC_weinreich TEM MIC weinreich -0.2531919803
## 576 TEM_MIC_weinreich TEM MIC weinreich -0.9029548338
## 577 TEM_MIC_weinreich TEM MIC weinreich 0.2946085787
## 578 TEM_MIC_weinreich TEM MIC weinreich 0.1589193176
## 579 TEM_MIC_weinreich TEM MIC weinreich 0.0340169486
## 580 TEM_MIC_weinreich TEM MIC weinreich 0.5607717676
## 581 TEM_MIC_weinreich TEM MIC weinreich 0.0323045028
## 582 TEM_MIC_weinreich TEM MIC weinreich -0.7790332707
## 583 TEM_MIC_weinreich TEM MIC weinreich -0.0333338299
## 584 TEM_MIC_weinreich TEM MIC weinreich 0.4991308025
## 585 TEM_MIC_weinreich TEM MIC weinreich 0.4495140405
## 586 TEM_MIC_weinreich TEM MIC weinreich -0.2029847382
## 587 TEM_MIC_weinreich TEM MIC weinreich 0.1679882204
## 588 TEM_MIC_weinreich TEM MIC weinreich 1.5870060093
## 589 TEM_MIC_weinreich TEM MIC weinreich -1.1900805252
## 590 TEM_MIC_weinreich TEM MIC weinreich -0.9755374929
## 591 TEM_MIC_weinreich TEM MIC weinreich -0.8185917634
## 592 TEM_MIC_weinreich TEM MIC weinreich 0.1233783106
## traj_effects access access_no_idio access_mean access_w_mean access_w_idio
## 1 0.643452676 NA NA NA NA NA
## 2 1.383815366 2 2 2 0 0
## 3 1.910624405 3 1 1 -2 2
## 4 2.324282455 3 4 4 1 -1
## 5 2.103803721 0 0 0 0 0
## 6 2.158362492 2 6 2 0 -4
## 7 2.294466226 3 0 1 -2 3
## 8 2.222716471 4 4 1 -3 0
## 9 2.313867220 5 0 0 -5 5
## 10 2.227886705 4 4 4 0 0
## 11 2.071882007 3 1 1 -2 2
## 12 2.184691431 3 1 2 -1 2
## 13 2.278753601 4 2 2 -2 2
## 14 -3.031517051 0 3 1 1 -3
## 15 1.856124444 3 3 3 0 0
## 16 1.710963119 1 1 4 3 0
## 17 1.983175072 3 1 0 -3 2
## 18 1.146128036 2 2 2 0 0
## 19 2.322219295 3 0 0 -3 3
## 20 2.324282455 4 1 0 -4 3
## 21 2.326335861 3 4 4 1 -1
## 22 2.086359831 3 3 0 -3 0
## 23 2.217483944 4 1 1 -3 3
## 24 2.049218023 3 2 2 -1 1
## 25 1.933993164 2 2 2 0 0
## 26 2.320146286 3 3 3 0 0
## 27 2.311753861 3 1 4 1 2
## 28 2.283301229 3 0 3 0 3
## 29 1.713490543 2 2 2 0 0
## 30 2.082785370 3 3 3 0 0
## 31 1.557507202 2 2 2 0 0
## 32 0.728353782 NA NA NA NA NA
## 33 -3.031517051 0 2 2 2 -2
## 34 0.717670503 1 1 1 0 0
## 35 2.324282455 4 4 2 -2 0
## 36 -0.798602876 1 1 0 -1 0
## 37 -0.422508200 0 4 4 4 -4
## 38 1.187520721 3 1 1 -2 2
## 39 1.973589623 4 4 4 0 0
## 40 1.363611980 3 0 1 -2 3
## 41 1.187520721 1 2 1 0 -1
## 42 -0.804100348 0 1 2 2 -1
## 43 -3.031517051 0 0 0 0 0
## 44 -0.277366077 0 0 0 0 0
## 45 0.706717782 1 2 2 1 -1
## 46 -0.543633967 0 2 2 2 -2
## 47 0.822821645 1 2 2 1 -1
## 48 0.307496038 NA NA NA NA NA
## 49 2.079181246 2 3 1 -1 -1
## 50 -0.228412519 0 0 0 0 0
## 51 2.049218023 2 1 1 -1 1
## 52 1.025305865 2 2 2 0 0
## 53 1.557507202 2 3 0 -2 -1
## 54 1.049218023 1 2 2 1 -1
## 55 1.824125834 1 2 1 0 -1
## 56 -0.157390760 0 2 3 3 -2
## 57 1.692846919 1 2 1 0 -1
## 58 0.267171728 NA NA NA NA NA
## 59 1.964730921 2 2 1 -1 0
## 60 1.472756449 NA NA NA NA NA
## 61 0.352182518 NA NA NA NA NA
## 62 1.025305865 2 2 2 0 0
## 63 3.082785370 3 3 3 0 0
## 64 4.389166084 4 4 4 0 0
## 65 4.281033367 4 4 4 0 0
## 66 2.606381365 3 3 3 0 0
## 67 4.658964843 4 4 4 0 0
## 68 2.799340549 3 3 3 0 0
## 69 1.700703717 2 2 2 0 0
## 70 2.870988814 3 3 3 0 0
## 71 4.198657087 4 0 4 0 4
## 72 2.774516966 3 3 3 0 0
## 73 1.484299839 2 2 2 0 0
## 74 3.049218023 3 3 3 0 0
## 75 1.804139432 2 2 2 0 0
## 76 0.342422681 NA NA NA NA NA
## 77 1.828015064 2 2 2 0 0
## 78 3.017033339 3 3 3 0 0
## 79 1.324282455 2 2 2 0 0
## 80 3.675778342 3 3 3 0 0
## 81 1.283301229 2 2 2 0 0
## 82 1.828015064 2 2 2 0 0
## 83 3.096910013 3 3 3 0 0
## 84 0.346352974 NA NA NA NA NA
## 85 2.738780558 2 2 2 0 0
## 86 0.620136055 NA NA NA NA NA
## 87 0.352182518 NA NA NA NA NA
## 88 1.025305865 2 2 2 0 0
## 89 3.082785370 3 3 3 0 0
## 90 3.962369336 4 4 4 0 0
## 91 2.037426498 3 3 3 0 0
## 92 4.117271296 4 4 4 0 0
## 93 2.799340549 3 3 3 0 0
## 94 1.700703717 2 2 2 0 0
## 95 2.981365509 3 3 3 0 0
## 96 4.110589710 4 2 4 0 2
## 97 2.774516966 3 3 3 0 0
## 98 1.643452676 2 2 2 0 0
## 99 2.450249108 3 3 3 0 0
## 100 1.804139432 2 2 2 0 0
## 101 0.342422681 NA NA NA NA NA
## 102 1.828015064 2 2 2 0 0
## 103 2.820857989 3 3 3 0 0
## 104 3.986771734 4 4 4 0 0
## 105 1.252853031 2 2 2 0 0
## 106 2.840733235 3 3 3 0 0
## 107 1.283301229 2 2 2 0 0
## 108 2.301029996 2 2 2 0 0
## 109 3.056904851 3 3 3 0 0
## 110 0.359835482 NA NA NA NA NA
## 111 1.075546961 2 2 2 0 0
## 112 0.620136055 NA NA NA NA NA
## 113 0.271841607 NA NA NA NA NA
## 114 0.755112266 2 2 2 0 0
## 115 1.107209970 3 1 2 -1 2
## 116 -0.395773947 1 1 1 0 0
## 117 -0.094743951 2 2 2 0 0
## 118 0.980457892 2 2 2 0 0
## 119 0.551449998 1 1 1 0 0
## 120 0.857935265 2 1 3 1 1
## 121 0.987666265 1 2 2 1 -1
## 122 -0.787812396 NA NA NA NA NA
## 123 -0.153044675 1 1 1 0 0
## 124 0.668385917 NA NA NA NA NA
## 125 0.930949031 NA NA NA NA NA
## 126 1.158362492 2 2 2 0 0
## 127 1.206825876 3 3 3 0 0
## 128 0.544068044 2 2 2 0 0
## 129 0.176091259 2 2 2 0 0
## 130 2.079181246 3 3 3 0 0
## 131 2.204119983 3 1 3 0 2
## 132 1.100370545 2 2 2 0 0
## 133 0.110589710 1 1 1 0 0
## 134 1.392696953 4 4 4 0 0
## 135 0.708420900 0 0 1 1 0
## 136 -0.331614083 1 1 1 0 0
## 137 0.348304863 2 0 2 0 2
## 138 0.710963119 1 2 2 1 -1
## 139 -0.031984286 NA NA NA NA NA
## 140 -0.144480844 0 0 2 2 0
## 141 -0.204119983 0 0 0 0 0
## 142 -0.173277480 2 3 3 1 -1
## 143 -0.455931956 1 1 1 0 0
## 144 0.071882007 3 4 3 0 -1
## 145 -0.155522824 2 1 2 0 1
## 146 -0.118045029 2 3 3 1 -1
## 147 0.326335861 3 3 3 0 0
## 148 -0.196542884 1 1 2 1 0
## 149 -0.131355562 2 0 0 -2 2
## 150 -0.402304814 1 3 3 2 -2
## 151 0.260071388 3 1 3 0 2
## 152 -0.071604148 3 4 4 1 -1
## 153 -0.081445469 2 0 1 -1 2
## 154 0.459392488 3 3 3 0 0
## 155 -0.177178355 0 0 0 0 0
## 156 -0.077274542 0 0 0 0 0
## 157 -0.343901798 0 0 0 0 0
## 158 -0.661543506 0 2 2 2 -2
## 159 -0.368556231 1 1 1 0 0
## 160 -0.080398976 2 3 3 1 -1
## 161 -0.534617149 0 0 0 0 0
## 162 -0.151810883 1 3 3 2 -2
## 163 -0.090443971 0 0 0 0 0
## 164 -0.241845378 0 0 0 0 0
## 165 -0.542118103 0 0 0 0 0
## 166 -0.232844134 1 3 3 2 -2
## 167 -0.073657553 1 1 0 -1 0
## 168 -0.501689446 0 1 1 1 -1
## 169 0.037426498 2 1 1 -1 1
## 170 -0.059981845 0 0 2 2 0
## 171 -0.012333735 NA NA NA NA NA
## 172 -0.053056729 0 0 0 0 0
## 173 0.004321374 3 0 0 -3 3
## 174 0.235528447 2 4 4 2 -2
## 175 0.537819095 4 2 2 -2 2
## 176 0.017033339 3 4 4 1 -1
## 177 0.423245874 2 2 2 0 0
## 178 0.688419822 4 4 4 0 0
## 179 -0.095284455 0 0 0 0 0
## 180 -0.026872146 0 0 2 2 0
## 181 0.296665190 2 2 1 -1 0
## 182 0.523746467 3 4 4 1 -1
## 183 0.033423755 3 1 1 -2 2
## 184 0.457881897 2 1 2 0 1
## 185 0.542825427 2 3 3 1 -1
## 186 -0.093126465 0 0 0 0 0
## 187 -0.010995384 NA NA NA NA NA
## 188 -0.085656843 0 0 0 0 0
## 189 0.158362492 1 1 1 0 0
## 190 0.434568904 2 4 4 2 -2
## 191 -0.006123085 3 0 0 -3 3
## 192 0.334453751 1 1 1 0 0
## 193 0.610660163 3 2 2 -1 1
## 194 -0.069050969 0 0 1 1 0
## 195 -0.008773924 NA NA NA NA NA
## 196 0.212187604 1 1 1 0 0
## 197 0.488550717 2 2 2 0 0
## 198 -0.034798299 1 0 1 0 1
## 199 0.582063363 2 1 2 0 1
## 200 -0.054039296 NA NA NA NA NA
## 201 0.056904851 NA NA NA NA NA
## 202 0.075546961 2 1 2 0 1
## 203 -0.083546051 1 1 0 -1 0
## 204 -0.024108864 2 2 2 0 0
## 205 -0.077274542 1 4 4 3 -3
## 206 0.734799830 4 6 6 2 -2
## 207 0.979092901 7 4 7 0 3
## 208 -0.144480844 2 0 4 2 2
## 209 0.737987326 4 4 4 0 0
## 210 0.975891136 5 5 6 1 0
## 211 -0.171340103 2 2 4 2 0
## 212 0.004321374 3 4 4 1 -1
## 213 0.758911892 4 2 3 -1 2
## 214 0.915927212 5 6 6 1 -1
## 215 -0.089375595 1 1 1 0 0
## 216 0.804139432 4 4 4 0 0
## 217 -0.208309351 0 0 0 0 0
## 218 0.075546961 3 1 2 -1 2
## 219 -0.053547735 3 3 3 0 0
## 220 0.434568904 4 2 2 -2 2
## 221 0.598790507 3 6 5 2 -3
## 222 0.004321374 5 5 3 -2 0
## 223 0.374748346 2 3 4 2 -1
## 224 0.752048448 4 3 3 -1 1
## 225 0.004321374 3 3 4 1 0
## 226 -0.071604148 2 0 0 -2 2
## 227 0.396199347 2 4 4 2 -2
## 228 0.668385917 4 2 2 -2 2
## 229 -0.022733788 4 4 4 0 0
## 230 0.413299764 2 1 2 0 1
## 231 0.762678564 4 4 4 0 0
## 232 -0.029653124 2 2 0 -2 0
## 233 0.060697840 2 1 1 -1 1
## 234 0.033423755 2 1 0 -2 1
## 235 0.068185862 4 4 4 0 0
## 236 0.661812686 4 2 3 -1 2
## 237 0.874481818 6 6 6 0 0
## 238 -0.024108864 2 1 4 2 1
## 239 0.691081492 3 4 4 1 -1
## 240 0.831869774 4 4 4 0 0
## 241 -0.083546051 1 3 3 2 -2
## 242 0.041392685 2 0 1 -1 2
## 243 0.647382970 3 4 4 1 -1
## 244 0.867467488 5 4 2 -3 1
## 245 0.029383778 2 4 4 2 -2
## 246 0.736396502 3 3 3 0 0
## 247 -0.076238039 1 1 0 -1 0
## 248 -0.016373713 0 1 2 2 -1
## 249 -0.181114585 0 0 0 0 0
## 250 0.530199698 2 4 4 2 -2
## 251 0.654176542 4 2 2 -2 2
## 252 -0.067019178 2 3 3 1 -1
## 253 0.564666064 2 2 2 0 0
## 254 0.712649702 3 4 4 1 -1
## 255 -0.034798299 2 0 0 -2 2
## 256 -0.151810883 0 1 2 2 -1
## 257 0.562292864 2 1 2 0 1
## 258 0.632457292 3 4 4 1 -1
## 259 -0.166215625 0 0 0 0 0
## 260 -0.040481623 1 1 0 -1 0
## 261 -0.071604148 NA NA NA NA NA
## 262 -0.013676223 1 0 1 0 1
## 263 -0.077274542 0 2 3 3 -2
## 264 -0.077274542 3 0 0 -3 3
## 265 0.459392488 4 3 4 0 1
## 266 0.636487896 4 4 6 2 0
## 267 -0.019088062 3 5 3 0 -2
## 268 -0.065501549 2 1 1 -1 1
## 269 0.521138084 3 5 2 -1 -2
## 270 0.113943352 2 0 1 -1 2
## 271 -0.155522824 1 3 3 2 -2
## 272 0.453318340 3 3 3 0 0
## 273 0.732393760 5 5 5 0 0
## 274 -0.107905397 2 0 0 -2 2
## 275 0.029383778 2 1 1 -1 1
## 276 0.526339277 4 2 2 -2 2
## 277 -0.010995384 1 3 3 2 -2
## 278 -0.074172425 0 1 0 0 -1
## 279 -0.166215625 1 2 2 1 -1
## 280 -0.208309351 1 1 0 -1 0
## 281 0.409933123 2 5 5 3 -3
## 282 -0.071604148 2 0 0 -2 2
## 283 -0.372634143 0 0 0 0 0
## 284 0.423245874 2 2 2 0 0
## 285 0.167317335 2 3 3 1 -1
## 286 -0.250263684 1 0 0 -1 1
## 287 -0.111259039 2 2 2 0 0
## 288 0.557507202 4 2 2 -2 2
## 289 -0.196542884 2 2 2 0 0
## 290 -0.327902142 0 1 1 1 -1
## 291 0.481442629 2 1 2 0 1
## 292 0.143014800 2 0 2 0 2
## 293 -0.012333735 NA NA NA NA NA
## 294 0.113943352 2 1 1 -1 1
## 295 0.103803721 2 3 3 1 -1
## 296 0.636487896 3 4 3 0 -1
## 297 0.868644438 5 5 2 -3 0
## 298 0.064457989 2 2 2 0 0
## 299 0.488550717 3 3 3 0 0
## 300 0.667452953 4 1 2 -2 3
## 301 0.245512668 3 3 3 0 0
## 302 -0.101823517 1 0 1 0 1
## 303 0.708420900 3 3 3 0 0
## 304 0.507855872 3 2 3 0 1
## 305 -0.065501549 2 2 2 0 0
## 306 0.463892989 1 1 2 1 0
## 307 0.503790683 2 3 2 0 -1
## 308 0.004321374 2 0 0 -2 2
## 309 0.021189299 NA NA NA NA NA
## 310 -0.141462802 1 1 0 -1 0
## 311 0.537819095 3 3 3 0 0
## 312 0.665580991 4 1 2 -2 3
## 313 -0.065501549 2 2 2 0 0
## 314 0.477121255 1 2 1 0 -1
## 315 0.654176542 3 3 3 0 0
## 316 0.209515015 2 1 1 -1 1
## 317 -0.301899454 NA NA NA NA NA
## 318 0.505149978 2 1 2 0 1
## 319 0.367355921 1 3 3 2 -2
## 320 -0.292429824 1 0 0 -1 1
## 321 0.599883072 2 1 1 -1 1
## 322 -0.078313525 NA NA NA NA NA
## 323 0.056904851 NA NA NA NA NA
## 324 0.086359831 2 1 2 0 1
## 325 0.008600172 1 3 2 1 -2
## 326 0.029383778 2 1 2 0 1
## 327 -0.166215625 2 2 0 -2 0
## 328 0.979548375 5 6 6 1 -1
## 329 0.155336037 5 6 6 1 -1
## 330 0.935003151 5 3 2 -3 2
## 331 0.929929560 5 6 6 1 -1
## 332 0.723455672 5 0 2 -3 5
## 333 -0.173277480 0 0 0 0 0
## 334 0.941511433 5 5 2 -3 0
## 335 0.103803721 4 0 0 -4 4
## 336 0.696356389 3 4 3 0 -1
## 337 0.836324116 4 1 1 -3 3
## 338 0.372912003 4 4 4 0 0
## 339 0.225309282 3 3 3 0 0
## 340 -0.101823517 3 1 1 -2 2
## 341 0.727541257 4 5 2 -2 -1
## 342 0.816903839 5 6 6 1 -1
## 343 -0.018634491 3 0 0 -3 3
## 344 0.627365857 3 3 4 1 0
## 345 0.851869601 5 0 2 -3 5
## 346 0.397940009 4 4 4 0 0
## 347 -0.118045029 2 2 2 0 0
## 348 0.705863712 3 4 4 1 -1
## 349 0.803457116 4 1 3 -1 3
## 350 -0.095284455 2 4 4 2 -2
## 351 0.582063363 2 1 2 0 1
## 352 0.824776462 4 4 4 0 0
## 353 0.313867220 3 1 1 -2 2
## 354 0.060697840 2 1 1 -1 1
## 355 0.146128036 2 3 3 1 -1
## 356 -0.224753740 1 0 1 0 1
## 357 1.000000000 5 1 1 -4 4
## 358 0.912753304 4 6 6 2 -2
## 359 0.107209970 4 0 0 -4 4
## 360 0.795880017 3 3 4 1 0
## 361 0.883093359 4 0 0 -4 4
## 362 0.359835482 4 4 4 0 0
## 363 -0.144480844 1 2 1 0 -1
## 364 0.649334859 1 4 4 3 -3
## 365 0.021189299 3 4 4 1 -1
## 366 0.278753601 3 1 1 -2 2
## 367 0.178976947 2 2 2 0 0
## 368 -0.287350298 0 2 2 2 -2
## 369 0.786751422 4 1 2 -2 3
## 370 0.800717078 5 1 1 -4 4
## 371 0.012837225 3 4 3 0 -1
## 372 0.745074792 3 3 3 0 0
## 373 0.602059991 1 4 4 3 -3
## 374 0.348304863 3 1 1 -2 2
## 375 -0.192464972 1 1 0 -1 0
## 376 0.720159303 3 3 3 0 0
## 377 0.758154622 4 4 4 0 0
## 378 -0.016373713 3 0 0 -3 3
## 379 0.742725131 3 1 1 -2 2
## 380 -0.449771647 0 1 1 1 -1
## 381 0.075546961 NA NA NA NA NA
## 382 0.264817823 2 1 1 -1 1
## 383 -0.019996628 NA NA NA NA NA
## 384 0.113943352 NA NA NA NA NA
## 385 0.045322979 0 2 2 2 -2
## 386 0.193124598 2 2 3 1 0
## 387 0.060697840 1 1 1 0 0
## 388 0.424881637 2 2 2 0 0
## 389 1.064457989 3 2 3 0 1
## 390 1.041392685 2 3 3 1 -1
## 391 1.041392685 2 2 2 0 0
## 392 0.383815366 NA NA NA NA NA
## 393 0.416640507 2 1 2 0 1
## 394 0.938019097 3 3 2 -1 0
## 395 1.365487985 4 4 4 0 0
## 396 1.146128036 3 2 3 0 1
## 397 0.773054693 2 2 2 0 0
## 398 -0.012333735 NA NA NA NA NA
## 399 0.513217600 2 1 2 0 1
## 400 1.041392685 3 3 3 0 0
## 401 0.068185862 1 1 2 1 0
## 402 0.770852012 2 2 2 0 0
## 403 0.139879086 NA NA NA NA NA
## 404 0.008600172 NA NA NA NA NA
## 405 0.195899652 1 2 1 0 -1
## 406 0.462397998 3 2 2 -1 1
## 407 0.691965103 3 3 3 0 0
## 408 1.012837225 4 4 4 0 0
## 409 0.755874856 3 3 3 0 0
## 410 0.332438460 2 2 2 0 0
## 411 0.238046103 2 0 1 -1 2
## 412 0.100370545 0 2 2 2 -2
## 413 0.017033339 2 1 2 0 1
## 414 0.374748346 2 2 2 0 0
## 415 0.764922985 3 3 3 0 0
## 416 0.146128036 2 2 3 1 0
## 417 0.149219113 1 2 1 0 -1
## 418 0.217483944 2 2 2 0 0
## 419 0.158362492 2 2 0 -2 0
## 420 0.086359831 2 1 2 0 1
## 421 0.472756449 2 2 2 0 0
## 422 0.758154622 3 3 3 0 0
## 423 0.997823081 4 5 5 1 -1
## 424 1.260071388 4 4 4 0 0
## 425 0.713490543 3 2 2 -1 1
## 426 1.322219295 4 5 4 0 -1
## 427 0.915399835 3 3 3 0 0
## 428 0.008600172 1 3 0 -1 -2
## 429 0.589949601 4 3 2 -2 1
## 430 0.898725182 4 5 5 1 -1
## 431 0.274157849 2 2 1 -1 0
## 432 0.570542940 3 2 3 0 1
## 433 0.840106094 3 2 3 0 1
## 434 0.365487985 2 3 3 1 -1
## 435 -0.008773924 NA NA NA NA NA
## 436 0.683947131 2 1 2 0 1
## 437 0.820201459 3 3 3 0 0
## 438 1.113943352 4 3 4 0 1
## 439 1.568201724 4 4 4 0 0
## 440 0.841984805 3 3 3 0 0
## 441 1.361727836 3 3 3 0 0
## 442 -0.014573526 0 0 0 0 0
## 443 0.478566496 1 3 1 0 -2
## 444 0.980003372 3 3 2 -1 0
## 445 0.281033367 2 2 2 0 0
## 446 0.539076099 2 1 2 0 1
## 447 0.898176483 3 3 3 0 0
## 448 0.380211242 2 1 2 0 1
## 449 0.004321374 NA NA NA NA NA
## 450 0.152288344 2 2 1 -1 0
## 451 0.621176282 3 3 3 0 0
## 452 0.611723308 2 4 4 2 -2
## 453 0.501059262 4 0 1 -3 4
## 454 0.376576957 2 3 4 2 -1
## 455 0.089905111 2 3 3 1 -1
## 456 0.243038049 3 4 4 1 -1
## 457 0.278753601 3 3 3 0 0
## 458 0.557507202 2 2 2 0 0
## 459 0.557507202 3 2 2 -1 1
## 460 0.460897843 2 4 4 2 -2
## 461 0.369215857 2 2 1 -1 0
## 462 0.004321374 1 2 0 -1 -1
## 463 0.110589710 3 0 0 -3 3
## 464 -0.031517051 0 2 2 2 -2
## 465 0.045322979 NA NA NA NA NA
## 466 0.506505032 2 2 1 -1 0
## 467 0.654176542 3 2 2 -1 1
## 468 0.442479769 1 4 4 3 -3
## 469 0.466867620 2 2 1 -1 0
## 470 0.086359831 2 2 2 0 0
## 471 0.170261715 3 3 2 -1 0
## 472 0.155336037 2 2 2 0 0
## 473 0.403120521 1 2 2 1 -1
## 474 0.481442629 3 1 1 -2 2
## 475 0.352182518 1 2 1 0 -1
## 476 0.025305865 NA NA NA NA NA
## 477 0.033423755 1 2 2 1 -1
## 478 0.041392685 NA NA NA NA NA
## 479 0.056904851 NA NA NA NA NA
## 480 0.064457989 2 2 2 0 0
## 481 -0.008773924 0 3 2 2 -3
## 482 1.170261715 3 3 3 0 0
## 483 1.130333768 3 3 2 -1 0
## 484 1.212187604 4 6 4 0 -2
## 485 1.232996110 4 2 3 -1 2
## 486 0.117271296 3 0 2 -1 3
## 487 0.212187604 2 2 0 -2 0
## 488 0.269512944 3 4 4 1 -1
## 489 1.332438460 3 4 4 1 -1
## 490 1.276461804 3 2 2 -1 1
## 491 -0.006563770 0 0 0 0 0
## 492 0.053078443 1 4 2 1 -3
## 493 0.201397124 3 1 2 -1 2
## 494 0.133538908 2 2 0 -2 0
## 495 1.123851641 3 3 2 -1 0
## 496 1.093421685 3 4 4 1 -1
## 497 1.110589710 4 2 2 -2 2
## 498 0.967079734 2 4 4 2 -2
## 499 0.127104798 2 2 3 1 0
## 500 0.243038049 4 4 4 0 0
## 501 0.235528447 3 3 3 0 0
## 502 1.071882007 3 2 2 -1 1
## 503 1.217483944 4 4 4 0 0
## 504 1.149219113 3 2 2 -1 1
## 505 0.012837225 0 2 1 1 -2
## 506 0.075546961 3 0 0 -3 3
## 507 0.053078443 1 2 2 1 -1
## 508 0.613841822 NA NA NA NA NA
## 509 2.717670503 2 2 2 0 0
## 510 2.401400541 2 3 2 0 -1
## 511 2.799340549 3 3 2 -1 0
## 512 3.198657087 4 5 5 1 -1
## 513 2.999130541 1 1 1 0 0
## 514 3.107209970 5 5 5 0 0
## 515 3.100370545 4 5 4 0 -1
## 516 2.831229694 4 2 2 -2 2
## 517 2.912222057 3 3 3 0 0
## 518 3.117271296 4 4 4 0 0
## 519 3.000000000 4 1 5 1 3
## 520 2.685741739 3 3 3 0 0
## 521 2.812244697 3 2 3 0 1
## 522 2.187520721 2 3 3 1 -1
## 523 0.572871602 1 1 1 0 0
## 524 0.570542940 1 3 3 2 -2
## 525 1.685741739 4 2 4 0 2
## 526 2.017033339 4 2 4 0 2
## 527 0.550228353 3 1 4 1 2
## 528 1.672097858 2 2 2 0 0
## 529 2.190331698 4 4 4 0 0
## 530 0.532754379 2 2 3 1 0
## 531 0.651278014 2 2 1 -1 0
## 532 1.664641976 2 2 2 0 0
## 533 0.474216264 0 3 1 1 -3
## 534 0.492760389 2 3 2 0 -1
## 535 1.951823035 2 2 2 0 0
## 536 1.367355921 2 2 2 0 0
## 537 0.481442629 1 1 1 0 0
## 538 0.820857989 NA NA NA NA NA
## 539 0.820201459 1 1 1 0 0
## 540 1.382017043 3 3 2 -1 0
## 541 2.950851459 4 4 3 -1 0
## 542 3.071882007 5 5 5 0 0
## 543 1.596597096 4 2 2 -2 2
## 544 2.113943352 2 3 2 0 -1
## 545 2.600972896 4 4 1 -3 0
## 546 0.945960704 2 3 1 -1 -1
## 547 1.267171728 2 2 2 0 0
## 548 2.725094521 3 3 3 0 0
## 549 2.634477270 3 4 4 1 -1
## 550 1.488550717 3 3 3 0 0
## 551 1.791690649 2 2 2 0 0
## 552 0.956648579 2 1 2 0 1
## 553 -0.193820026 NA NA NA NA NA
## 554 0.149219113 1 1 1 0 0
## 555 1.336459734 2 2 2 0 0
## 556 1.683047038 4 4 4 0 0
## 557 0.068185862 1 2 1 0 -1
## 558 1.235528447 1 1 2 1 0
## 559 1.630427875 3 1 2 -1 2
## 560 -0.194499142 0 0 2 2 0
## 561 0.274157849 NA NA NA NA NA
## 562 1.367355921 1 2 2 1 -1
## 563 1.158362492 2 2 2 0 0
## 564 0.371067862 2 1 1 -1 1
## 565 0.691081492 1 1 1 0 0
## 566 -0.065501549 NA NA NA NA NA
## 567 0.178976947 NA NA NA NA NA
## 568 0.269512944 2 1 0 -2 1
## 569 3.611723308 3 3 3 0 0
## 570 -0.090979146 NA NA NA NA NA
## 571 2.561101384 2 1 2 0 1
## 572 0.000000000 NA NA NA NA NA
## 573 1.201397124 2 1 2 0 1
## 574 0.962369336 1 3 3 2 -2
## 575 1.201397124 2 0 0 -2 2
## 576 3.611723308 3 3 3 0 0
## 577 2.416640507 2 2 2 0 0
## 578 0.000000000 NA NA NA NA NA
## 579 0.311753861 2 2 2 0 0
## 580 0.311753861 3 3 3 0 0
## 581 4.378397901 4 4 4 0 0
## 582 3.611723308 3 3 2 -1 0
## 583 0.000000000 2 0 0 -2 2
## 584 3.611723308 3 3 3 0 0
## 585 1.201397124 2 2 2 0 0
## 586 0.000000000 2 2 0 -2 0
## 587 1.371067862 3 3 3 0 0
## 588 1.201397124 3 0 3 0 3
## 589 4.285557309 3 4 4 1 -1
## 590 0.000000000 2 3 2 0 -1
## 591 3.611723308 4 4 4 0 0
## 592 3.611723308 3 2 3 0 1
Now I can ask a variety of questions with this dataset. For example, lets see where epistasis changes node visitation
dim(adaptive_traj_access %>%
drop_na() %>%
filter(access != access_no_idio))[1] / dim(adaptive_traj_access %>% drop_na())[1]
## [1] 0.4939966
paste0(dim(adaptive_traj_access %>%
drop_na() %>%
filter(access != access_no_idio))[1], "/", dim(adaptive_traj_access %>% drop_na())[1])
## [1] "288/583"
How often does epistasis changes node visitation for adaptive trajectories
dim(adaptive_traj_access %>%
filter(likely) %>%
drop_na() %>%
filter(access != access_no_idio))[1] / dim(adaptive_traj_access %>% filter(likely) %>% drop_na())[1]
## [1] 0.2619048
paste0(dim(adaptive_traj_access %>%
filter(likely) %>%
drop_na() %>%
filter(access != access_no_idio))[1], "/", dim(adaptive_traj_access %>% filter(likely) %>% drop_na())[1])
## [1] "11/42"
Where is idiosyncratic visitation different from the mean WHEN epistasis affects node visitation?
dim(adaptive_traj_access %>%
drop_na() %>%
filter(access != access_mean & access != access_no_idio))[1] / dim(adaptive_traj_access %>% filter(access != access_no_idio) %>% drop_na())[1]
## [1] 0.7326389
paste0(dim(adaptive_traj_access %>%
drop_na() %>%
filter(access != access_mean & access != access_no_idio))[1], "/", dim(adaptive_traj_access %>% filter(access != access_no_idio) %>% drop_na())[1])
## [1] "211/288"
Where is idiosyncratic visitation different from the mean in the likely traj?
dim(adaptive_traj_access %>%
filter(likely) %>%
drop_na() %>%
filter(access != access_mean))[1] / dim(adaptive_traj_access %>% filter(likely) %>% drop_na())[1]
## [1] 0.1904762
paste0(dim(adaptive_traj_access %>%
filter(likely) %>%
drop_na() %>%
filter(access != access_mean))[1], "/", dim(adaptive_traj_access %>% filter(likely) %>% drop_na())[1])
## [1] "8/42"
Restrictive idiosyncrasy: When negative epistasis prevents node visitation AND mean epistasis would have allowed for it
adaptive_traj_access %>%
drop_na() %>%
filter(avg < log10(1/1.5) & access_w_idio < 0 & access_w_mean > 0 & means > 0)
## genotype avg pos mutations likely
## 1 xxxx00 -3.4400740 p1|p21|p26|p28 4 FALSE
## 2 x0xx0x -1.1042305 p1|p26|p28|p94 4 FALSE
## 3 0xx000 -4.0673669 p21|p26 2 FALSE
## 4 0x0xx0 -1.6714200 p21|p28|p30 3 FALSE
## 5 0x00x0 -0.2888077 p21|p30 2 FALSE
## 6 0x00xx -1.5696224 p21|p30|p94 3 FALSE
## 7 00x00x -0.7310345 p26|p94 2 FALSE
## 8 000xxx -2.0436471 p28|p30|p94 3 FALSE
## 9 x000x -0.8883718 p72|p273 2 FALSE
## 10 00xxxx0 -0.1976298 p219|p222|p224|p225 4 FALSE
## 11 x0x0xx0 -0.2755877 p41|p219|p224|p225 4 FALSE
## 12 x00x0xx -0.1767230 p41|p222|p225|p227 4 FALSE
## 13 x000x00 -0.1999418 p41|p224 2 FALSE
## 14 xxxxxx0 -0.8084870 p41|p215|p219|p222|p224|p225 6 FALSE
## 15 xxxxx0x -0.9215912 p41|p215|p219|p222|p224|p227 6 FALSE
## 16 xxx0xxx -0.7393002 p41|p215|p219|p224|p225|p227 6 TRUE
## 17 xx0xxxx -0.3881689 p41|p215|p222|p224|p225|p227 6 FALSE
## 18 0xxx0x -0.1911406 p142|p154|p158|p215 4 FALSE
## 19 0x0xxx -0.2428085 p142|p158|p180|p215 4 FALSE
## 20 00xxxx -0.3383319 p154|p158|p180|p215 4 FALSE
## 21 x0xx0x -0.3904590 p67|p154|p158|p215 4 FALSE
## 22 0xxx0 -1.6219401 p42|p104|p182 3 FALSE
## unique_id enzyme_name measure_type cond means
## 1 DHFR_ic75_palmer DHFR ic75 palmer 0.086939990
## 2 DHFR_ic75_palmer DHFR ic75 palmer 2.573205564
## 3 DHFR_ic75_palmer DHFR ic75 palmer 0.535154647
## 4 DHFR_ic75_palmer DHFR ic75 palmer 0.760258028
## 5 DHFR_ic75_palmer DHFR ic75 palmer 0.298883333
## 6 DHFR_ic75_palmer DHFR ic75 palmer 0.279451613
## 7 DHFR_ic75_palmer DHFR ic75 palmer 0.496677313
## 8 DHFR_ic75_palmer DHFR ic75 palmer 0.680059180
## 9 MPH_catact_ZnPTM MPH catact ZnPTM 0.602786109
## 10 NfsA_ec50_2039 NfsA ec50 2039 0.032862709
## 11 NfsA_ec50_2039 NfsA ec50 2039 0.051240783
## 12 NfsA_ec50_2039 NfsA ec50 2039 0.025115068
## 13 NfsA_ec50_2039 NfsA ec50 2039 0.058499915
## 14 NfsA_ec50_3637 NfsA ec50 3637 0.148040734
## 15 NfsA_ec50_3637 NfsA ec50 3637 0.034936572
## 16 NfsA_ec50_3637 NfsA ec50 3637 0.217227524
## 17 NfsA_ec50_3637 NfsA ec50 3637 0.568358874
## 18 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.164689964
## 19 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.017996043
## 20 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.078829122
## 21 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.004661701
## 22 TEM_MIC_weinreich TEM MIC weinreich 0.134901726
## traj_effects access access_no_idio access_mean access_w_mean access_w_idio
## 1 2.3242825 3 4 4 1 -1
## 2 2.3263359 3 4 4 1 -1
## 3 -3.0315171 0 2 2 2 -2
## 4 -0.8041003 0 1 2 2 -1
## 5 0.7067178 1 2 2 1 -1
## 6 -0.5436340 0 2 2 2 -2
## 7 1.0492180 1 2 2 1 -1
## 8 -0.1573908 0 2 3 3 -2
## 9 0.7109631 1 2 2 1 -1
## 10 0.2355284 2 4 4 2 -2
## 11 0.6473830 3 4 4 1 -1
## 12 0.7126497 3 4 4 1 -1
## 13 -0.1518109 0 1 2 2 -1
## 14 0.9795484 5 6 6 1 -1
## 15 0.1553360 5 6 6 1 -1
## 16 0.9454686 5 6 6 1 -1
## 17 0.8169038 5 6 6 1 -1
## 18 0.3765770 2 3 4 2 -1
## 19 0.4608978 2 4 4 2 -2
## 20 0.4424798 1 4 4 3 -3
## 21 0.9670797 2 4 4 2 -2
## 22 0.9623693 1 3 3 2 -2
What about positive epistasis that does not affect or increases visitation?
adaptive_traj_access %>%
drop_na() %>%
filter(avg > log10(1.5) & access_w_idio >= 0)
## genotype avg pos mutations likely
## 1 xxx000 4.0915006 p1|p21|p26 3 FALSE
## 2 xxxxx0 0.2127319 p1|p21|p26|p28|p30 5 FALSE
## 3 xxxx0x 10.4358638 p1|p21|p26|p28|p94 5 FALSE
## 4 xxx0xx 2.5755328 p1|p21|p26|p30|p94 5 FALSE
## 5 xx0x00 0.7938784 p1|p21|p28 3 FALSE
## 6 xx0xx0 1.9958051 p1|p21|p28|p30 4 FALSE
## 7 xx0xxx 3.1289862 p1|p21|p28|p30|p94 5 FALSE
## 8 xx00xx 1.3698610 p1|p21|p30|p94 4 FALSE
## 9 xx000x 1.0635938 p1|p21|p94 3 FALSE
## 10 x0x000 0.1951793 p1|p26 2 FALSE
## 11 x0xxx0 0.2566377 p1|p26|p28|p30 4 FALSE
## 12 x0xxxx 0.5135761 p1|p26|p28|p30|p94 5 FALSE
## 13 x0x0xx 0.2071172 p1|p26|p30|p94 4 FALSE
## 14 x0x00x 0.7200699 p1|p26|p94 3 FALSE
## 15 x00xxx 2.2307063 p1|p28|p30|p94 4 FALSE
## 16 x00x0x 1.0706822 p1|p28|p94 3 FALSE
## 17 x000x0 0.8028661 p1|p30 2 FALSE
## 18 0xxx00 3.2599112 p21|p26|p28 3 FALSE
## 19 0xxxxx 0.9545832 p21|p26|p28|p30|p94 5 FALSE
## 20 0xx0x0 3.7900357 p21|p26|p30 3 FALSE
## 21 0xx00x 5.0316956 p21|p26|p94 3 FALSE
## 22 0x0xxx 2.6259368 p21|p28|p30|p94 4 FALSE
## 23 00xx0x 0.7498001 p26|p28|p94 3 FALSE
## 24 00x0x0 0.4506381 p26|p30 2 FALSE
## 25 0000xx 0.2248027 p30|p94 2 FALSE
## 26 xx000 0.3307007 p21|p26 2 FALSE
## 27 xxx00 0.3536997 p21|p26|p28 3 FALSE
## 28 xx0xx 0.5272617 p21|p26|p30|p94 4 FALSE
## 29 x0x00 0.2181874 p21|p28 2 FALSE
## 30 x0xxx 1.8737920 p21|p28|p30|p94 4 FALSE
## 31 x00x0 0.7857643 p21|p30 2 FALSE
## 32 x000x 0.8318209 p21|p94 2 FALSE
## 33 0xx00 0.3552586 p26|p28 2 FALSE
## 34 0xxxx 0.3612176 p26|p28|p30|p94 4 TRUE
## 35 0x0x0 0.6355068 p26|p30 2 FALSE
## 36 0x00x 0.3207425 p26|p94 2 FALSE
## 37 00xx0 0.3513283 p28|p30 2 FALSE
## 38 00x0x 0.6093657 p28|p94 2 TRUE
## 39 000xx 1.7722915 p30|p94 2 FALSE
## 40 xx000 0.3307007 p21|p26 2 FALSE
## 41 xxx00 0.3536997 p21|p26|p28 3 FALSE
## 42 xxxx0 0.6065199 p21|p26|p28|p30 4 FALSE
## 43 xx0xx 0.3995444 p21|p26|p30|p94 4 FALSE
## 44 x0x00 0.2181874 p21|p28 2 FALSE
## 45 x0xxx 1.2697737 p21|p28|p30|p94 4 FALSE
## 46 x00x0 0.9314347 p21|p30 2 FALSE
## 47 x000x 0.8318209 p21|p94 2 FALSE
## 48 0xx00 0.3552586 p26|p28 2 FALSE
## 49 0x0x0 0.5505949 p26|p30 2 FALSE
## 50 0x0xx 0.5514262 p26|p30|p94 3 FALSE
## 51 0x00x 0.3207425 p26|p94 2 FALSE
## 52 00xx0 0.8108607 p28|p30 2 FALSE
## 53 00x0x 0.6093657 p28|p94 2 TRUE
## 54 0xxxx 0.3792063 p193|p258|p271|p273 4 FALSE
## 55 00xx0 0.3449452 p258|p271 2 FALSE
## 56 00xxx 0.3467544 p258|p271|p273 3 FALSE
## 57 xxx0x 0.2309903 p72|p193|p258|p273 4 TRUE
## 58 xx0xx 0.3313957 p72|p193|p271|p273 4 FALSE
## 59 xx00x 1.2255130 p72|p193|p273 3 FALSE
## 60 x0xxx 0.4273977 p72|p258|p271|p273 4 FALSE
## 61 x0x0x 0.5030731 p72|p258|p273 3 FALSE
## 62 x00xx 0.9335231 p72|p271|p273 3 FALSE
## 63 0xxxxx0 0.1926166 p215|p219|p222|p224|p225 5 FALSE
## 64 0xx0x00 0.2287509 p215|p219|p224 3 FALSE
## 65 0xx0xxx 0.5419563 p215|p219|p224|p225|p227 5 FALSE
## 66 0xx00x0 0.4841288 p215|p219|p225 3 FALSE
## 67 0x0xxxx 0.2850142 p215|p222|p224|p225|p227 5 FALSE
## 68 0x00xx0 0.3700748 p215|p224|p225 3 FALSE
## 69 0x000xx 0.3526148 p215|p225|p227 3 FALSE
## 70 00xxxxx 0.2066060 p219|p222|p224|p225|p227 5 FALSE
## 71 00000xx 0.2144987 p225|p227 2 FALSE
## 72 xxxxxxx 0.2315052 p41|p215|p219|p222|p224|p225|p227 7 FALSE
## 73 xxxxx0x 0.1907594 p41|p215|p219|p222|p224|p227 6 FALSE
## 74 xxx0xx0 0.5158592 p41|p215|p219|p224|p225 5 FALSE
## 75 xx0xxx0 0.2094495 p41|p215|p222|p224|p225 5 FALSE
## 76 xx0x0xx 0.3418076 p41|p215|p222|p225|p227 5 FALSE
## 77 xx00x00 0.2626518 p41|p215|p224 3 FALSE
## 78 xx00xxx 0.4214522 p41|p215|p224|p225|p227 5 FALSE
## 79 xx000x0 0.6668734 p41|p215|p225 3 FALSE
## 80 x0xxxx0 0.2249481 p41|p219|p222|p224|p225 5 FALSE
## 81 x0x0x00 0.1951751 p41|p219|p224 3 FALSE
## 82 x0x0xxx 0.2083878 p41|p219|p224|p225|p227 5 FALSE
## 83 x00xxxx 0.2077501 p41|p222|p224|p225|p227 5 FALSE
## 84 x000xx0 0.3525577 p41|p224|p225 3 FALSE
## 85 0xxx00x 0.3133913 p215|p219|p222|p227 4 FALSE
## 86 0xx0x0x 0.2739510 p215|p219|p224|p227 4 FALSE
## 87 0xx00x0 0.3031382 p215|p219|p225 3 FALSE
## 88 0x0xx0x 0.2141675 p215|p222|p224|p227 4 FALSE
## 89 0x00xxx 0.3657730 p215|p224|p225|p227 4 FALSE
## 90 0x000xx 0.3965362 p215|p225|p227 3 FALSE
## 91 0x0000x 0.2929325 p215|p227 2 FALSE
## 92 00xx0xx 0.2335504 p219|p222|p225|p227 4 FALSE
## 93 00x0x00 0.2124097 p219|p224 2 FALSE
## 94 000xxxx 0.4085244 p222|p224|p225|p227 4 FALSE
## 95 000x00x 0.2666392 p222|p227 2 FALSE
## 96 0000xx0 0.3270425 p224|p225 2 FALSE
## 97 00000xx 0.1981897 p225|p227 2 FALSE
## 98 xxxxxxx 1.9130555 p41|p215|p219|p222|p224|p225|p227 7 TRUE
## 99 xxxx0x0 0.3058285 p41|p215|p219|p222|p225 5 FALSE
## 100 xxxx00x 0.7515982 p41|p215|p219|p222|p227 5 FALSE
## 101 xxx0xx0 0.2205264 p41|p215|p219|p224|p225 5 FALSE
## 102 xxx0x0x 0.5787185 p41|p215|p219|p224|p227 5 FALSE
## 103 xxx00xx 0.4055680 p41|p215|p219|p225|p227 5 FALSE
## 104 xx0xx0x 0.4530458 p41|p215|p222|p224|p227 5 FALSE
## 105 xx0x0xx 0.7581143 p41|p215|p222|p225|p227 5 FALSE
## 106 xx00xxx 0.4101789 p41|p215|p224|p225|p227 5 FALSE
## 107 xx000x0 0.6177405 p41|p215|p225 3 FALSE
## 108 xx0000x 0.4412514 p41|p215|p227 3 FALSE
## 109 x0xxxx0 0.4244484 p41|p219|p222|p224|p225 5 FALSE
## 110 x0xxx0x 0.7129843 p41|p219|p222|p224|p227 5 FALSE
## 111 x0xx0xx 0.6719119 p41|p219|p222|p225|p227 5 FALSE
## 112 x0x0xxx 0.9140294 p41|p219|p224|p225|p227 5 TRUE
## 113 x0x000x 0.6297636 p41|p219|p227 3 FALSE
## 114 x00xxx0 0.3133401 p41|p222|p224|p225 4 FALSE
## 115 x00xxxx 0.3339212 p41|p222|p224|p225|p227 5 FALSE
## 116 x00x00x 0.4093652 p41|p222|p227 3 FALSE
## 117 x000x0x 0.5949846 p41|p224|p227 3 FALSE
## 118 x0000xx 0.3800391 p41|p225|p227 3 FALSE
## 119 00xx 0.2092675 p212|p213 2 FALSE
## 120 xxxx 0.2724234 p33|p72|p212|p213 4 TRUE
## 121 0xx0 0.6098199 p72|p212 2 FALSE
## 122 0x0x 0.8038499 p72|p213 2 TRUE
## 123 00xx0x 0.3996077 p124|p132|p243 3 FALSE
## 124 00x00x 0.2493602 p124|p243 2 FALSE
## 125 000xxx 0.2022888 p132|p156|p243 3 FALSE
## 126 0000xx 0.2453667 p156|p243 2 FALSE
## 127 x0xx00 0.2129063 p66|p124|p132 3 FALSE
## 128 x0x0xx 0.4265923 p66|p124|p156|p243 4 FALSE
## 129 xxxxxx 0.2371716 p66|p76|p124|p132|p156|p243 6 TRUE
## 130 xxx0x0 0.1837956 p66|p76|p124|p156 4 FALSE
## 131 xxx00x 0.3693714 p66|p76|p124|p243 4 FALSE
## 132 xx00x0 0.1813204 p66|p76|p156 3 FALSE
## 133 xx00xx 0.3471839 p66|p76|p156|p243 4 FALSE
## 134 0xx000 0.3089057 p76|p124 2 FALSE
## 135 0xxxx0 0.2279391 p76|p124|p132|p156 4 FALSE
## 136 0xx0xx 0.3817072 p76|p124|p156|p243 4 TRUE
## 137 0x000x 0.2491061 p76|p243 2 FALSE
## 138 0xxxxx 0.4960242 p142|p154|p158|p180|p215 5 FALSE
## 139 000xxx 0.1923837 p158|p180|p215 3 FALSE
## 140 xxxx0x 0.4519437 p67|p142|p154|p158|p215 5 FALSE
## 141 xxx0x0 0.2375477 p67|p142|p154|p180 4 FALSE
## 142 xx0x00 0.2148131 p67|p142|p158 3 TRUE
## 143 xx0xxx 0.2618915 p67|p142|p158|p180|p215 5 FALSE
## 144 xx000x 0.2179967 p67|p142|p215 3 FALSE
## 145 x0xxxx 0.5746045 p67|p154|p158|p180|p215 5 FALSE
## 146 x00x00 0.5040028 p67|p158 2 TRUE
## 147 x00xx0 0.1772274 p67|p158|p180 3 FALSE
## 148 x00x0x 0.2813481 p67|p158|p215 3 FALSE
## 149 xx0000 1.2829707 p233|p254 2 FALSE
## 150 xxxx00 0.1965328 p233|p254|p271|p272 4 FALSE
## 151 xxxxxx 0.2725240 p233|p254|p271|p272|p306|p313 6 FALSE
## 152 xxx0x0 0.8786927 p233|p254|p271|p306 4 TRUE
## 153 xxx00x 0.9425507 p233|p254|p271|p313 4 FALSE
## 154 xx0xxx 0.4836946 p233|p254|p272|p306|p313 5 FALSE
## 155 xx0x0x 0.4066111 p233|p254|p272|p313 4 FALSE
## 156 xx00xx 0.6635153 p233|p254|p306|p313 4 FALSE
## 157 x0xxx0 0.2559400 p233|p271|p272|p306 4 FALSE
## 158 x0xxxx 0.6985225 p233|p271|p272|p306|p313 5 FALSE
## 159 x0xx0x 0.2886868 p233|p271|p272|p313 4 FALSE
## 160 x000xx 0.2113048 p233|p306|p313 3 FALSE
## 161 0xx000 0.1931635 p254|p271 2 FALSE
## 162 0xxx0x 0.2459981 p254|p271|p272|p313 4 FALSE
## 163 0x0xx0 0.3960234 p254|p272|p306 3 FALSE
## 164 0x000x 0.2012921 p254|p313 2 FALSE
## 165 00x0xx 1.0589513 p271|p306|p313 3 FALSE
## 166 000xxx 0.3574693 p272|p306|p313 3 FALSE
## 167 00xx0 0.1815151 p104|p182 2 FALSE
## 168 00x0x 2.2313492 p104|p238 2 TRUE
## 169 000xx 1.4506834 p182|p238 2 FALSE
## 170 0xx00 1.0224202 p42|p104 2 FALSE
## 171 0xxxx 1.9266776 p42|p104|p182|p238 4 TRUE
## 172 0x0x0 1.2923763 p42|p182 2 FALSE
## 173 0x00x 1.2152434 p42|p238 2 FALSE
## 174 x00xx 0.9596428 p4205|p182|p238 3 FALSE
## 175 xxxx0 1.4522693 p4205|p42|p104|p182 4 FALSE
## 176 xxxxx 0.2694734 p4205|p42|p104|p182|p238 5 TRUE
## 177 xx00x 1.1950828 p4205|p42|p238 3 FALSE
## unique_id enzyme_name measure_type cond means
## 1 DHFR_ic75_palmer DHFR ic75 palmer 0.217216184
## 2 DHFR_ic75_palmer DHFR ic75 palmer -3.381835882
## 3 DHFR_ic75_palmer DHFR ic75 palmer 6.841295986
## 4 DHFR_ic75_palmer DHFR ic75 palmer -1.019035014
## 5 DHFR_ic75_palmer DHFR ic75 palmer 0.010454317
## 6 DHFR_ic75_palmer DHFR ic75 palmer 1.869380289
## 7 DHFR_ic75_palmer DHFR ic75 palmer -0.465581551
## 8 DHFR_ic75_palmer DHFR ic75 palmer 2.424836638
## 9 DHFR_ic75_palmer DHFR ic75 palmer -0.373479384
## 10 DHFR_ic75_palmer DHFR ic75 palmer 0.215500788
## 11 DHFR_ic75_palmer DHFR ic75 palmer -1.177492205
## 12 DHFR_ic75_palmer DHFR ic75 palmer -3.080991709
## 13 DHFR_ic75_palmer DHFR ic75 palmer -0.045612248
## 14 DHFR_ic75_palmer DHFR ic75 palmer 0.102700261
## 15 DHFR_ic75_palmer DHFR ic75 palmer 2.254703577
## 16 DHFR_ic75_palmer DHFR ic75 palmer 1.647841559
## 17 DHFR_ic75_palmer DHFR ic75 palmer 0.695195042
## 18 DHFR_ic75_palmer DHFR ic75 palmer 1.124524104
## 19 DHFR_ic75_palmer DHFR ic75 palmer -2.639984560
## 20 DHFR_ic75_palmer DHFR ic75 palmer 0.947297526
## 21 DHFR_ic75_palmer DHFR ic75 palmer 1.695299221
## 22 DHFR_ic75_palmer DHFR ic75 palmer 2.870437650
## 23 DHFR_ic75_palmer DHFR ic75 palmer -0.043087603
## 24 DHFR_ic75_palmer DHFR ic75 palmer 0.352814894
## 25 DHFR_ic75_palmer DHFR ic75 palmer -0.563214885
## 26 DHFR_ki_trajg DHFR ki trajg 0.410912753
## 27 DHFR_ki_trajg DHFR ki trajg 0.082006660
## 28 DHFR_ki_trajg DHFR ki trajg 0.326060583
## 29 DHFR_ki_trajg DHFR ki trajg 0.077303838
## 30 DHFR_ki_trajg DHFR ki trajg 1.672590918
## 31 DHFR_ki_trajg DHFR ki trajg 0.214034606
## 32 DHFR_ki_trajg DHFR ki trajg 0.015658816
## 33 DHFR_ki_trajg DHFR ki trajg 0.402406020
## 34 DHFR_ki_trajg DHFR ki trajg 0.160016473
## 35 DHFR_ki_trajg DHFR ki trajg 0.418956845
## 36 DHFR_ki_trajg DHFR ki trajg 0.256944120
## 37 DHFR_ki_trajg DHFR ki trajg -0.277507064
## 38 DHFR_ki_trajg DHFR ki trajg -0.297834483
## 39 DHFR_ki_trajg DHFR ki trajg 0.535607716
## 40 DHFR_ki_trajr DHFR ki trajr 0.239167595
## 41 DHFR_ki_trajr DHFR ki trajr 0.445570792
## 42 DHFR_ki_trajr DHFR ki trajr 0.357234391
## 43 DHFR_ki_trajr DHFR ki trajr 0.150258886
## 44 DHFR_ki_trajr DHFR ki trajr -0.146072962
## 45 DHFR_ki_trajr DHFR ki trajr 1.020488239
## 46 DHFR_ki_trajr DHFR ki trajr 0.242098088
## 47 DHFR_ki_trajr DHFR ki trajr 0.280000986
## 48 DHFR_ki_trajr DHFR ki trajr 0.092249759
## 49 DHFR_ki_trajr DHFR ki trajr 0.126911640
## 50 DHFR_ki_trajr DHFR ki trajr 0.407566173
## 51 DHFR_ki_trajr DHFR ki trajr 0.469744755
## 52 DHFR_ki_trajr DHFR ki trajr 0.048558239
## 53 DHFR_ki_trajr DHFR ki trajr -0.078811299
## 54 MPH_catact_ZnPTM MPH catact ZnPTM 0.029835956
## 55 MPH_catact_ZnPTM MPH catact ZnPTM 0.575971300
## 56 MPH_catact_ZnPTM MPH catact ZnPTM 0.575371168
## 57 MPH_catact_ZnPTM MPH catact ZnPTM -0.118379979
## 58 MPH_catact_ZnPTM MPH catact ZnPTM -0.017974583
## 59 MPH_catact_ZnPTM MPH catact ZnPTM 1.332020910
## 60 MPH_catact_ZnPTM MPH catact ZnPTM 0.078027364
## 61 MPH_catact_ZnPTM MPH catact ZnPTM 0.657581922
## 62 MPH_catact_ZnPTM MPH catact ZnPTM 1.138234602
## 63 NfsA_ec50_2039 NfsA ec50 2039 0.091641808
## 64 NfsA_ec50_2039 NfsA ec50 2039 -0.036461257
## 65 NfsA_ec50_2039 NfsA ec50 2039 0.245504340
## 66 NfsA_ec50_2039 NfsA ec50 2039 0.351777786
## 67 NfsA_ec50_2039 NfsA ec50 2039 0.042804203
## 68 NfsA_ec50_2039 NfsA ec50 2039 -0.017781959
## 69 NfsA_ec50_2039 NfsA ec50 2039 0.139765328
## 70 NfsA_ec50_2039 NfsA ec50 2039 0.091283562
## 71 NfsA_ec50_2039 NfsA ec50 2039 0.288989481
## 72 NfsA_ec50_2039 NfsA ec50 2039 0.231505206
## 73 NfsA_ec50_2039 NfsA ec50 2039 0.306511983
## 74 NfsA_ec50_2039 NfsA ec50 2039 0.282532026
## 75 NfsA_ec50_2039 NfsA ec50 2039 0.030364300
## 76 NfsA_ec50_2039 NfsA ec50 2039 0.214665214
## 77 NfsA_ec50_2039 NfsA ec50 2039 0.089121883
## 78 NfsA_ec50_2039 NfsA ec50 2039 0.046889824
## 79 NfsA_ec50_2039 NfsA ec50 2039 0.528651949
## 80 NfsA_ec50_2039 NfsA ec50 2039 0.172750482
## 81 NfsA_ec50_2039 NfsA ec50 2039 0.051632061
## 82 NfsA_ec50_2039 NfsA ec50 2039 -0.039287044
## 83 NfsA_ec50_2039 NfsA ec50 2039 0.014317246
## 84 NfsA_ec50_2039 NfsA ec50 2039 0.170826259
## 85 NfsA_ec50_3637 NfsA ec50 3637 0.160300084
## 86 NfsA_ec50_3637 NfsA ec50 3637 0.092614626
## 87 NfsA_ec50_3637 NfsA ec50 3637 0.218717203
## 88 NfsA_ec50_3637 NfsA ec50 3637 0.012205919
## 89 NfsA_ec50_3637 NfsA ec50 3637 0.104497453
## 90 NfsA_ec50_3637 NfsA ec50 3637 0.168111203
## 91 NfsA_ec50_3637 NfsA ec50 3637 0.187697910
## 92 NfsA_ec50_3637 NfsA ec50 3637 0.061186698
## 93 NfsA_ec50_3637 NfsA ec50 3637 0.074854163
## 94 NfsA_ec50_3637 NfsA ec50 3637 0.167571165
## 95 NfsA_ec50_3637 NfsA ec50 3637 0.063311553
## 96 NfsA_ec50_3637 NfsA ec50 3637 0.312612193
## 97 NfsA_ec50_3637 NfsA ec50 3637 0.052353689
## 98 NfsA_ec50_3637 NfsA ec50 3637 1.913055479
## 99 NfsA_ec50_3637 NfsA ec50 3637 -0.182582709
## 100 NfsA_ec50_3637 NfsA ec50 3637 0.206634861
## 101 NfsA_ec50_3637 NfsA ec50 3637 -0.075103365
## 102 NfsA_ec50_3637 NfsA ec50 3637 0.226536706
## 103 NfsA_ec50_3637 NfsA ec50 3637 -0.048249881
## 104 NfsA_ec50_3637 NfsA ec50 3637 0.276429636
## 105 NfsA_ec50_3637 NfsA ec50 3637 0.479862128
## 106 NfsA_ec50_3637 NfsA ec50 3637 0.324708216
## 107 NfsA_ec50_3637 NfsA ec50 3637 0.209513563
## 108 NfsA_ec50_3637 NfsA ec50 3637 -0.181880262
## 109 NfsA_ec50_3637 NfsA ec50 3637 -0.198513642
## 110 NfsA_ec50_3637 NfsA ec50 3637 0.033470171
## 111 NfsA_ec50_3637 NfsA ec50 3637 -0.109238294
## 112 NfsA_ec50_3637 NfsA ec50 3637 0.325660765
## 113 NfsA_ec50_3637 NfsA ec50 3637 0.224891005
## 114 NfsA_ec50_3637 NfsA ec50 3637 0.246350434
## 115 NfsA_ec50_3637 NfsA ec50 3637 -0.078881834
## 116 NfsA_ec50_3637 NfsA ec50 3637 -0.055554618
## 117 NfsA_ec50_3637 NfsA ec50 3637 0.120640826
## 118 NfsA_ec50_3637 NfsA ec50 3637 -0.360855472
## 119 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 -0.001843610
## 120 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.272423449
## 121 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.452007376
## 122 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.529802289
## 123 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.077667202
## 124 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.146937967
## 125 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.024577444
## 126 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.037403037
## 127 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.155031553
## 128 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.386238225
## 129 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.237171612
## 130 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.002308696
## 131 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.233908794
## 132 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.310315577
## 133 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.100521064
## 134 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.268755433
## 135 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.120377866
## 136 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.230852093
## 137 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.197798300
## 138 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.259717480
## 139 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.041536847
## 140 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.215636991
## 141 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.164932025
## 142 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.174011928
## 143 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.025584752
## 144 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.072315748
## 145 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.338297731
## 146 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.662941716
## 147 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.010609253
## 148 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.121161418
## 149 PTE_catact_2NH PTE catact 2NH 0.637336423
## 150 PTE_catact_2NH PTE catact 2NH -0.495675095
## 151 PTE_catact_2NH PTE catact 2NH 0.272524024
## 152 PTE_catact_2NH PTE catact 2NH -0.333523949
## 153 PTE_catact_2NH PTE catact 2NH -0.226875448
## 154 PTE_catact_2NH PTE catact 2NH 0.619956569
## 155 PTE_catact_2NH PTE catact 2NH 0.357815158
## 156 PTE_catact_2NH PTE catact 2NH 0.094710621
## 157 PTE_catact_2NH PTE catact 2NH 0.271767578
## 158 PTE_catact_2NH PTE catact 2NH 0.834784556
## 159 PTE_catact_2NH PTE catact 2NH 0.347304857
## 160 PTE_catact_2NH PTE catact 2NH -0.059630116
## 161 PTE_catact_2NH PTE catact 2NH 0.013758529
## 162 PTE_catact_2NH PTE catact 2NH -0.230150706
## 163 PTE_catact_2NH PTE catact 2NH 0.175202752
## 164 PTE_catact_2NH PTE catact 2NH 0.102229722
## 165 PTE_catact_2NH PTE catact 2NH 0.476490309
## 166 PTE_catact_2NH PTE catact 2NH -0.094957390
## 167 TEM_MIC_weinreich TEM MIC weinreich -0.638009314
## 168 TEM_MIC_weinreich TEM MIC weinreich 0.817397805
## 169 TEM_MIC_weinreich TEM MIC weinreich 0.682809629
## 170 TEM_MIC_weinreich TEM MIC weinreich 0.041619164
## 171 TEM_MIC_weinreich TEM MIC weinreich 2.061414245
## 172 TEM_MIC_weinreich TEM MIC weinreich -0.253191980
## 173 TEM_MIC_weinreich TEM MIC weinreich 0.294608579
## 174 TEM_MIC_weinreich TEM MIC weinreich 0.499130803
## 175 TEM_MIC_weinreich TEM MIC weinreich 1.587006009
## 176 TEM_MIC_weinreich TEM MIC weinreich 0.269473383
## 177 TEM_MIC_weinreich TEM MIC weinreich 0.123378311
## traj_effects access access_no_idio access_mean access_w_mean access_w_idio
## 1 1.91062440 3 1 1 -2 2
## 2 2.10380372 0 0 0 0 0
## 3 2.29446623 3 0 1 -2 3
## 4 2.31386722 5 0 0 -5 5
## 5 2.07188201 3 1 1 -2 2
## 6 2.18469143 3 1 2 -1 2
## 7 2.27875360 4 2 2 -2 2
## 8 1.71096312 1 1 4 3 0
## 9 1.98317507 3 1 0 -3 2
## 10 1.14612804 2 2 2 0 0
## 11 2.32221929 3 0 0 -3 3
## 12 2.32428246 4 1 0 -4 3
## 13 2.21748394 4 1 1 -3 3
## 14 2.04921802 3 2 2 -1 1
## 15 2.31175386 3 1 4 1 2
## 16 2.28330123 3 0 3 0 3
## 17 1.71349054 2 2 2 0 0
## 18 0.71767050 1 1 1 0 0
## 19 -0.79860288 1 1 0 -1 0
## 20 1.18752072 3 1 1 -2 2
## 21 1.36361198 3 0 1 -2 3
## 22 -3.03151705 0 0 0 0 0
## 23 2.04921802 2 1 1 -1 1
## 24 1.02530587 2 2 2 0 0
## 25 1.96473092 2 2 1 -1 0
## 26 1.02530587 2 2 2 0 0
## 27 3.08278537 3 3 3 0 0
## 28 4.65896484 4 4 4 0 0
## 29 1.70070372 2 2 2 0 0
## 30 4.19865709 4 0 4 0 4
## 31 1.48429984 2 2 2 0 0
## 32 1.80413943 2 2 2 0 0
## 33 1.82801506 2 2 2 0 0
## 34 4.58206336 4 4 4 0 0
## 35 1.32428246 2 2 2 0 0
## 36 1.28330123 2 2 2 0 0
## 37 1.82801506 2 2 2 0 0
## 38 2.35983548 2 2 2 0 0
## 39 2.73878056 2 2 2 0 0
## 40 1.02530587 2 2 2 0 0
## 41 3.08278537 3 3 3 0 0
## 42 3.96236934 4 4 4 0 0
## 43 4.11727130 4 4 4 0 0
## 44 1.70070372 2 2 2 0 0
## 45 4.11058971 4 2 4 0 2
## 46 1.64345268 2 2 2 0 0
## 47 1.80413943 2 2 2 0 0
## 48 1.82801506 2 2 2 0 0
## 49 1.25285303 2 2 2 0 0
## 50 2.84073323 3 3 3 0 0
## 51 1.28330123 2 2 2 0 0
## 52 2.30103000 2 2 2 0 0
## 53 2.35983548 2 2 2 0 0
## 54 1.10720997 3 1 2 -1 2
## 55 0.55145000 1 1 1 0 0
## 56 0.85793526 2 1 3 1 1
## 57 2.31175386 4 3 3 -1 1
## 58 2.07918125 3 3 3 0 0
## 59 2.20411998 3 1 3 0 2
## 60 1.39269695 4 4 4 0 0
## 61 0.70842090 0 0 1 1 0
## 62 0.34830486 2 0 2 0 2
## 63 -0.45593196 1 1 1 0 0
## 64 -0.13135556 2 0 0 -2 2
## 65 0.26007139 3 1 3 0 2
## 66 -0.08144547 2 0 1 -1 2
## 67 -0.36855623 1 1 1 0 0
## 68 -0.54211810 0 0 0 0 0
## 69 0.03742650 2 1 1 -1 1
## 70 0.53781910 4 2 2 -2 2
## 71 0.58206336 2 1 2 0 1
## 72 0.97909290 7 4 7 0 3
## 73 -0.14448084 2 0 4 2 2
## 74 0.75891189 4 2 3 -1 2
## 75 0.43456890 4 2 2 -2 2
## 76 0.75204845 4 3 3 -1 1
## 77 -0.07160415 2 0 0 -2 2
## 78 0.66838592 4 2 2 -2 2
## 79 0.41329976 2 1 2 0 1
## 80 0.66181269 4 2 3 -1 2
## 81 0.04139269 2 0 1 -1 2
## 82 0.86746749 5 4 2 -3 1
## 83 0.65417654 4 2 2 -2 2
## 84 0.56229286 2 1 2 0 1
## 85 0.11394335 2 0 1 -1 2
## 86 -0.10790540 2 0 0 -2 2
## 87 0.02938378 2 1 1 -1 1
## 88 -0.07160415 2 0 0 -2 2
## 89 0.55750720 4 2 2 -2 2
## 90 0.48144263 2 1 2 0 1
## 91 0.14301480 2 0 2 0 2
## 92 0.66745295 4 1 2 -2 3
## 93 -0.10182352 1 0 1 0 1
## 94 0.66558099 4 1 2 -2 3
## 95 0.20951501 2 1 1 -1 1
## 96 0.50514998 2 1 2 0 1
## 97 0.59988307 2 1 1 -1 1
## 98 1.00432137 7 0 7 0 7
## 99 0.93500315 5 3 2 -3 2
## 100 0.72345567 5 0 2 -3 5
## 101 0.94151143 5 5 2 -3 0
## 102 0.10380372 4 0 0 -4 4
## 103 0.83632412 4 1 1 -3 3
## 104 -0.01863449 3 0 0 -3 3
## 105 0.85186960 5 0 2 -3 5
## 106 0.80345712 4 1 3 -1 3
## 107 0.58206336 2 1 2 0 1
## 108 0.31386722 3 1 1 -2 2
## 109 1.00000000 5 1 1 -4 4
## 110 0.10720997 4 0 0 -4 4
## 111 0.88309336 4 0 0 -4 4
## 112 0.95664858 5 1 1 -4 4
## 113 0.27875360 3 1 1 -2 2
## 114 0.78675142 4 1 2 -2 3
## 115 0.80071708 5 1 1 -4 4
## 116 0.34830486 3 1 1 -2 2
## 117 -0.01637371 3 0 0 -3 3
## 118 0.74272513 3 1 1 -2 2
## 119 0.26481782 2 1 1 -1 1
## 120 1.60422605 4 3 4 0 1
## 121 1.04139269 2 2 2 0 0
## 122 1.13987909 2 1 2 0 1
## 123 1.14612804 3 2 3 0 1
## 124 0.77305469 2 2 2 0 0
## 125 1.04139269 3 3 3 0 0
## 126 0.77085201 2 2 2 0 0
## 127 0.46239800 3 2 2 -1 1
## 128 0.23804610 2 0 1 -1 2
## 129 1.50785587 5 4 5 0 1
## 130 0.71349054 3 2 2 -1 1
## 131 0.91539984 3 3 3 0 0
## 132 0.57054294 3 2 3 0 1
## 133 0.84010609 3 2 3 0 1
## 134 0.68394713 2 1 2 0 1
## 135 1.11394335 4 3 4 0 1
## 136 1.52762990 4 3 4 0 1
## 137 0.38021124 2 1 2 0 1
## 138 0.50105926 4 0 1 -3 4
## 139 0.48144263 3 1 1 -2 2
## 140 1.23299611 4 2 3 -1 2
## 141 0.11727130 3 0 2 -1 3
## 142 1.33645973 3 3 3 0 0
## 143 1.27646180 3 2 2 -1 1
## 144 0.20139712 3 1 2 -1 2
## 145 1.11058971 4 2 2 -2 2
## 146 0.99387691 2 2 2 0 0
## 147 1.07188201 3 2 2 -1 1
## 148 1.14921911 3 2 2 -1 1
## 149 2.71767050 2 2 2 0 0
## 150 2.79934055 3 3 2 -1 0
## 151 2.99913054 1 1 1 0 0
## 152 3.24054925 4 2 1 -3 2
## 153 2.83122969 4 2 2 -2 2
## 154 3.00000000 4 1 5 1 3
## 155 2.68574174 3 3 3 0 0
## 156 2.81224470 3 2 3 0 1
## 157 1.68574174 4 2 4 0 2
## 158 2.01703334 4 2 4 0 2
## 159 0.55022835 3 1 4 1 2
## 160 1.36735592 2 2 2 0 0
## 161 0.82020146 1 1 1 0 0
## 162 1.59659710 4 2 2 -2 2
## 163 2.72509452 3 3 3 0 0
## 164 0.95664858 2 1 2 0 1
## 165 1.63042788 3 1 2 -1 2
## 166 1.15836249 2 2 2 0 0
## 167 0.26951294 2 1 0 -2 1
## 168 3.61172331 2 2 2 0 0
## 169 2.56110138 2 1 2 0 1
## 170 1.20139712 2 1 2 0 1
## 171 4.51851394 4 1 4 0 3
## 172 1.20139712 2 0 0 -2 2
## 173 2.41664051 2 2 2 0 0
## 174 3.61172331 3 3 3 0 0
## 175 1.20139712 3 0 3 0 3
## 176 4.66838592 5 4 5 0 1
## 177 3.61172331 3 2 3 0 1
adaptive_traj_access %>%
drop_na() %>%
filter(avg > log10(1.5) & access_w_idio > 0)
## genotype avg pos mutations likely
## 1 xxx000 4.0915006 p1|p21|p26 3 FALSE
## 2 xxxx0x 10.4358638 p1|p21|p26|p28|p94 5 FALSE
## 3 xxx0xx 2.5755328 p1|p21|p26|p30|p94 5 FALSE
## 4 xx0x00 0.7938784 p1|p21|p28 3 FALSE
## 5 xx0xx0 1.9958051 p1|p21|p28|p30 4 FALSE
## 6 xx0xxx 3.1289862 p1|p21|p28|p30|p94 5 FALSE
## 7 xx000x 1.0635938 p1|p21|p94 3 FALSE
## 8 x0xxx0 0.2566377 p1|p26|p28|p30 4 FALSE
## 9 x0xxxx 0.5135761 p1|p26|p28|p30|p94 5 FALSE
## 10 x0x0xx 0.2071172 p1|p26|p30|p94 4 FALSE
## 11 x0x00x 0.7200699 p1|p26|p94 3 FALSE
## 12 x00xxx 2.2307063 p1|p28|p30|p94 4 FALSE
## 13 x00x0x 1.0706822 p1|p28|p94 3 FALSE
## 14 0xx0x0 3.7900357 p21|p26|p30 3 FALSE
## 15 0xx00x 5.0316956 p21|p26|p94 3 FALSE
## 16 00xx0x 0.7498001 p26|p28|p94 3 FALSE
## 17 x0xxx 1.8737920 p21|p28|p30|p94 4 FALSE
## 18 x0xxx 1.2697737 p21|p28|p30|p94 4 FALSE
## 19 0xxxx 0.3792063 p193|p258|p271|p273 4 FALSE
## 20 00xxx 0.3467544 p258|p271|p273 3 FALSE
## 21 xxx0x 0.2309903 p72|p193|p258|p273 4 TRUE
## 22 xx00x 1.2255130 p72|p193|p273 3 FALSE
## 23 x00xx 0.9335231 p72|p271|p273 3 FALSE
## 24 0xx0x00 0.2287509 p215|p219|p224 3 FALSE
## 25 0xx0xxx 0.5419563 p215|p219|p224|p225|p227 5 FALSE
## 26 0xx00x0 0.4841288 p215|p219|p225 3 FALSE
## 27 0x000xx 0.3526148 p215|p225|p227 3 FALSE
## 28 00xxxxx 0.2066060 p219|p222|p224|p225|p227 5 FALSE
## 29 00000xx 0.2144987 p225|p227 2 FALSE
## 30 xxxxxxx 0.2315052 p41|p215|p219|p222|p224|p225|p227 7 FALSE
## 31 xxxxx0x 0.1907594 p41|p215|p219|p222|p224|p227 6 FALSE
## 32 xxx0xx0 0.5158592 p41|p215|p219|p224|p225 5 FALSE
## 33 xx0xxx0 0.2094495 p41|p215|p222|p224|p225 5 FALSE
## 34 xx0x0xx 0.3418076 p41|p215|p222|p225|p227 5 FALSE
## 35 xx00x00 0.2626518 p41|p215|p224 3 FALSE
## 36 xx00xxx 0.4214522 p41|p215|p224|p225|p227 5 FALSE
## 37 xx000x0 0.6668734 p41|p215|p225 3 FALSE
## 38 x0xxxx0 0.2249481 p41|p219|p222|p224|p225 5 FALSE
## 39 x0x0x00 0.1951751 p41|p219|p224 3 FALSE
## 40 x0x0xxx 0.2083878 p41|p219|p224|p225|p227 5 FALSE
## 41 x00xxxx 0.2077501 p41|p222|p224|p225|p227 5 FALSE
## 42 x000xx0 0.3525577 p41|p224|p225 3 FALSE
## 43 0xxx00x 0.3133913 p215|p219|p222|p227 4 FALSE
## 44 0xx0x0x 0.2739510 p215|p219|p224|p227 4 FALSE
## 45 0xx00x0 0.3031382 p215|p219|p225 3 FALSE
## 46 0x0xx0x 0.2141675 p215|p222|p224|p227 4 FALSE
## 47 0x00xxx 0.3657730 p215|p224|p225|p227 4 FALSE
## 48 0x000xx 0.3965362 p215|p225|p227 3 FALSE
## 49 0x0000x 0.2929325 p215|p227 2 FALSE
## 50 00xx0xx 0.2335504 p219|p222|p225|p227 4 FALSE
## 51 00x0x00 0.2124097 p219|p224 2 FALSE
## 52 000xxxx 0.4085244 p222|p224|p225|p227 4 FALSE
## 53 000x00x 0.2666392 p222|p227 2 FALSE
## 54 0000xx0 0.3270425 p224|p225 2 FALSE
## 55 00000xx 0.1981897 p225|p227 2 FALSE
## 56 xxxxxxx 1.9130555 p41|p215|p219|p222|p224|p225|p227 7 TRUE
## 57 xxxx0x0 0.3058285 p41|p215|p219|p222|p225 5 FALSE
## 58 xxxx00x 0.7515982 p41|p215|p219|p222|p227 5 FALSE
## 59 xxx0x0x 0.5787185 p41|p215|p219|p224|p227 5 FALSE
## 60 xxx00xx 0.4055680 p41|p215|p219|p225|p227 5 FALSE
## 61 xx0xx0x 0.4530458 p41|p215|p222|p224|p227 5 FALSE
## 62 xx0x0xx 0.7581143 p41|p215|p222|p225|p227 5 FALSE
## 63 xx00xxx 0.4101789 p41|p215|p224|p225|p227 5 FALSE
## 64 xx000x0 0.6177405 p41|p215|p225 3 FALSE
## 65 xx0000x 0.4412514 p41|p215|p227 3 FALSE
## 66 x0xxxx0 0.4244484 p41|p219|p222|p224|p225 5 FALSE
## 67 x0xxx0x 0.7129843 p41|p219|p222|p224|p227 5 FALSE
## 68 x0xx0xx 0.6719119 p41|p219|p222|p225|p227 5 FALSE
## 69 x0x0xxx 0.9140294 p41|p219|p224|p225|p227 5 TRUE
## 70 x0x000x 0.6297636 p41|p219|p227 3 FALSE
## 71 x00xxx0 0.3133401 p41|p222|p224|p225 4 FALSE
## 72 x00xxxx 0.3339212 p41|p222|p224|p225|p227 5 FALSE
## 73 x00x00x 0.4093652 p41|p222|p227 3 FALSE
## 74 x000x0x 0.5949846 p41|p224|p227 3 FALSE
## 75 x0000xx 0.3800391 p41|p225|p227 3 FALSE
## 76 00xx 0.2092675 p212|p213 2 FALSE
## 77 xxxx 0.2724234 p33|p72|p212|p213 4 TRUE
## 78 0x0x 0.8038499 p72|p213 2 TRUE
## 79 00xx0x 0.3996077 p124|p132|p243 3 FALSE
## 80 x0xx00 0.2129063 p66|p124|p132 3 FALSE
## 81 x0x0xx 0.4265923 p66|p124|p156|p243 4 FALSE
## 82 xxxxxx 0.2371716 p66|p76|p124|p132|p156|p243 6 TRUE
## 83 xxx0x0 0.1837956 p66|p76|p124|p156 4 FALSE
## 84 xx00x0 0.1813204 p66|p76|p156 3 FALSE
## 85 xx00xx 0.3471839 p66|p76|p156|p243 4 FALSE
## 86 0xx000 0.3089057 p76|p124 2 FALSE
## 87 0xxxx0 0.2279391 p76|p124|p132|p156 4 FALSE
## 88 0xx0xx 0.3817072 p76|p124|p156|p243 4 TRUE
## 89 0x000x 0.2491061 p76|p243 2 FALSE
## 90 0xxxxx 0.4960242 p142|p154|p158|p180|p215 5 FALSE
## 91 000xxx 0.1923837 p158|p180|p215 3 FALSE
## 92 xxxx0x 0.4519437 p67|p142|p154|p158|p215 5 FALSE
## 93 xxx0x0 0.2375477 p67|p142|p154|p180 4 FALSE
## 94 xx0xxx 0.2618915 p67|p142|p158|p180|p215 5 FALSE
## 95 xx000x 0.2179967 p67|p142|p215 3 FALSE
## 96 x0xxxx 0.5746045 p67|p154|p158|p180|p215 5 FALSE
## 97 x00xx0 0.1772274 p67|p158|p180 3 FALSE
## 98 x00x0x 0.2813481 p67|p158|p215 3 FALSE
## 99 xxx0x0 0.8786927 p233|p254|p271|p306 4 TRUE
## 100 xxx00x 0.9425507 p233|p254|p271|p313 4 FALSE
## 101 xx0xxx 0.4836946 p233|p254|p272|p306|p313 5 FALSE
## 102 xx00xx 0.6635153 p233|p254|p306|p313 4 FALSE
## 103 x0xxx0 0.2559400 p233|p271|p272|p306 4 FALSE
## 104 x0xxxx 0.6985225 p233|p271|p272|p306|p313 5 FALSE
## 105 x0xx0x 0.2886868 p233|p271|p272|p313 4 FALSE
## 106 0xxx0x 0.2459981 p254|p271|p272|p313 4 FALSE
## 107 0x000x 0.2012921 p254|p313 2 FALSE
## 108 00x0xx 1.0589513 p271|p306|p313 3 FALSE
## 109 00xx0 0.1815151 p104|p182 2 FALSE
## 110 000xx 1.4506834 p182|p238 2 FALSE
## 111 0xx00 1.0224202 p42|p104 2 FALSE
## 112 0xxxx 1.9266776 p42|p104|p182|p238 4 TRUE
## 113 0x0x0 1.2923763 p42|p182 2 FALSE
## 114 xxxx0 1.4522693 p4205|p42|p104|p182 4 FALSE
## 115 xxxxx 0.2694734 p4205|p42|p104|p182|p238 5 TRUE
## 116 xx00x 1.1950828 p4205|p42|p238 3 FALSE
## unique_id enzyme_name measure_type cond means
## 1 DHFR_ic75_palmer DHFR ic75 palmer 0.217216184
## 2 DHFR_ic75_palmer DHFR ic75 palmer 6.841295986
## 3 DHFR_ic75_palmer DHFR ic75 palmer -1.019035014
## 4 DHFR_ic75_palmer DHFR ic75 palmer 0.010454317
## 5 DHFR_ic75_palmer DHFR ic75 palmer 1.869380289
## 6 DHFR_ic75_palmer DHFR ic75 palmer -0.465581551
## 7 DHFR_ic75_palmer DHFR ic75 palmer -0.373479384
## 8 DHFR_ic75_palmer DHFR ic75 palmer -1.177492205
## 9 DHFR_ic75_palmer DHFR ic75 palmer -3.080991709
## 10 DHFR_ic75_palmer DHFR ic75 palmer -0.045612248
## 11 DHFR_ic75_palmer DHFR ic75 palmer 0.102700261
## 12 DHFR_ic75_palmer DHFR ic75 palmer 2.254703577
## 13 DHFR_ic75_palmer DHFR ic75 palmer 1.647841559
## 14 DHFR_ic75_palmer DHFR ic75 palmer 0.947297526
## 15 DHFR_ic75_palmer DHFR ic75 palmer 1.695299221
## 16 DHFR_ic75_palmer DHFR ic75 palmer -0.043087603
## 17 DHFR_ki_trajg DHFR ki trajg 1.672590918
## 18 DHFR_ki_trajr DHFR ki trajr 1.020488239
## 19 MPH_catact_ZnPTM MPH catact ZnPTM 0.029835956
## 20 MPH_catact_ZnPTM MPH catact ZnPTM 0.575371168
## 21 MPH_catact_ZnPTM MPH catact ZnPTM -0.118379979
## 22 MPH_catact_ZnPTM MPH catact ZnPTM 1.332020910
## 23 MPH_catact_ZnPTM MPH catact ZnPTM 1.138234602
## 24 NfsA_ec50_2039 NfsA ec50 2039 -0.036461257
## 25 NfsA_ec50_2039 NfsA ec50 2039 0.245504340
## 26 NfsA_ec50_2039 NfsA ec50 2039 0.351777786
## 27 NfsA_ec50_2039 NfsA ec50 2039 0.139765328
## 28 NfsA_ec50_2039 NfsA ec50 2039 0.091283562
## 29 NfsA_ec50_2039 NfsA ec50 2039 0.288989481
## 30 NfsA_ec50_2039 NfsA ec50 2039 0.231505206
## 31 NfsA_ec50_2039 NfsA ec50 2039 0.306511983
## 32 NfsA_ec50_2039 NfsA ec50 2039 0.282532026
## 33 NfsA_ec50_2039 NfsA ec50 2039 0.030364300
## 34 NfsA_ec50_2039 NfsA ec50 2039 0.214665214
## 35 NfsA_ec50_2039 NfsA ec50 2039 0.089121883
## 36 NfsA_ec50_2039 NfsA ec50 2039 0.046889824
## 37 NfsA_ec50_2039 NfsA ec50 2039 0.528651949
## 38 NfsA_ec50_2039 NfsA ec50 2039 0.172750482
## 39 NfsA_ec50_2039 NfsA ec50 2039 0.051632061
## 40 NfsA_ec50_2039 NfsA ec50 2039 -0.039287044
## 41 NfsA_ec50_2039 NfsA ec50 2039 0.014317246
## 42 NfsA_ec50_2039 NfsA ec50 2039 0.170826259
## 43 NfsA_ec50_3637 NfsA ec50 3637 0.160300084
## 44 NfsA_ec50_3637 NfsA ec50 3637 0.092614626
## 45 NfsA_ec50_3637 NfsA ec50 3637 0.218717203
## 46 NfsA_ec50_3637 NfsA ec50 3637 0.012205919
## 47 NfsA_ec50_3637 NfsA ec50 3637 0.104497453
## 48 NfsA_ec50_3637 NfsA ec50 3637 0.168111203
## 49 NfsA_ec50_3637 NfsA ec50 3637 0.187697910
## 50 NfsA_ec50_3637 NfsA ec50 3637 0.061186698
## 51 NfsA_ec50_3637 NfsA ec50 3637 0.074854163
## 52 NfsA_ec50_3637 NfsA ec50 3637 0.167571165
## 53 NfsA_ec50_3637 NfsA ec50 3637 0.063311553
## 54 NfsA_ec50_3637 NfsA ec50 3637 0.312612193
## 55 NfsA_ec50_3637 NfsA ec50 3637 0.052353689
## 56 NfsA_ec50_3637 NfsA ec50 3637 1.913055479
## 57 NfsA_ec50_3637 NfsA ec50 3637 -0.182582709
## 58 NfsA_ec50_3637 NfsA ec50 3637 0.206634861
## 59 NfsA_ec50_3637 NfsA ec50 3637 0.226536706
## 60 NfsA_ec50_3637 NfsA ec50 3637 -0.048249881
## 61 NfsA_ec50_3637 NfsA ec50 3637 0.276429636
## 62 NfsA_ec50_3637 NfsA ec50 3637 0.479862128
## 63 NfsA_ec50_3637 NfsA ec50 3637 0.324708216
## 64 NfsA_ec50_3637 NfsA ec50 3637 0.209513563
## 65 NfsA_ec50_3637 NfsA ec50 3637 -0.181880262
## 66 NfsA_ec50_3637 NfsA ec50 3637 -0.198513642
## 67 NfsA_ec50_3637 NfsA ec50 3637 0.033470171
## 68 NfsA_ec50_3637 NfsA ec50 3637 -0.109238294
## 69 NfsA_ec50_3637 NfsA ec50 3637 0.325660765
## 70 NfsA_ec50_3637 NfsA ec50 3637 0.224891005
## 71 NfsA_ec50_3637 NfsA ec50 3637 0.246350434
## 72 NfsA_ec50_3637 NfsA ec50 3637 -0.078881834
## 73 NfsA_ec50_3637 NfsA ec50 3637 -0.055554618
## 74 NfsA_ec50_3637 NfsA ec50 3637 0.120640826
## 75 NfsA_ec50_3637 NfsA ec50 3637 -0.360855472
## 76 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 -0.001843610
## 77 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.272423449
## 78 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 0.529802289
## 79 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.077667202
## 80 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.155031553
## 81 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.386238225
## 82 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.237171612
## 83 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.002308696
## 84 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.310315577
## 85 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.100521064
## 86 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.268755433
## 87 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.120377866
## 88 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.230852093
## 89 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.197798300
## 90 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.259717480
## 91 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.041536847
## 92 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.215636991
## 93 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.164932025
## 94 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.025584752
## 95 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.072315748
## 96 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.338297731
## 97 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.010609253
## 98 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.121161418
## 99 PTE_catact_2NH PTE catact 2NH -0.333523949
## 100 PTE_catact_2NH PTE catact 2NH -0.226875448
## 101 PTE_catact_2NH PTE catact 2NH 0.619956569
## 102 PTE_catact_2NH PTE catact 2NH 0.094710621
## 103 PTE_catact_2NH PTE catact 2NH 0.271767578
## 104 PTE_catact_2NH PTE catact 2NH 0.834784556
## 105 PTE_catact_2NH PTE catact 2NH 0.347304857
## 106 PTE_catact_2NH PTE catact 2NH -0.230150706
## 107 PTE_catact_2NH PTE catact 2NH 0.102229722
## 108 PTE_catact_2NH PTE catact 2NH 0.476490309
## 109 TEM_MIC_weinreich TEM MIC weinreich -0.638009314
## 110 TEM_MIC_weinreich TEM MIC weinreich 0.682809629
## 111 TEM_MIC_weinreich TEM MIC weinreich 0.041619164
## 112 TEM_MIC_weinreich TEM MIC weinreich 2.061414245
## 113 TEM_MIC_weinreich TEM MIC weinreich -0.253191980
## 114 TEM_MIC_weinreich TEM MIC weinreich 1.587006009
## 115 TEM_MIC_weinreich TEM MIC weinreich 0.269473383
## 116 TEM_MIC_weinreich TEM MIC weinreich 0.123378311
## traj_effects access access_no_idio access_mean access_w_mean access_w_idio
## 1 1.91062440 3 1 1 -2 2
## 2 2.29446623 3 0 1 -2 3
## 3 2.31386722 5 0 0 -5 5
## 4 2.07188201 3 1 1 -2 2
## 5 2.18469143 3 1 2 -1 2
## 6 2.27875360 4 2 2 -2 2
## 7 1.98317507 3 1 0 -3 2
## 8 2.32221929 3 0 0 -3 3
## 9 2.32428246 4 1 0 -4 3
## 10 2.21748394 4 1 1 -3 3
## 11 2.04921802 3 2 2 -1 1
## 12 2.31175386 3 1 4 1 2
## 13 2.28330123 3 0 3 0 3
## 14 1.18752072 3 1 1 -2 2
## 15 1.36361198 3 0 1 -2 3
## 16 2.04921802 2 1 1 -1 1
## 17 4.19865709 4 0 4 0 4
## 18 4.11058971 4 2 4 0 2
## 19 1.10720997 3 1 2 -1 2
## 20 0.85793526 2 1 3 1 1
## 21 2.31175386 4 3 3 -1 1
## 22 2.20411998 3 1 3 0 2
## 23 0.34830486 2 0 2 0 2
## 24 -0.13135556 2 0 0 -2 2
## 25 0.26007139 3 1 3 0 2
## 26 -0.08144547 2 0 1 -1 2
## 27 0.03742650 2 1 1 -1 1
## 28 0.53781910 4 2 2 -2 2
## 29 0.58206336 2 1 2 0 1
## 30 0.97909290 7 4 7 0 3
## 31 -0.14448084 2 0 4 2 2
## 32 0.75891189 4 2 3 -1 2
## 33 0.43456890 4 2 2 -2 2
## 34 0.75204845 4 3 3 -1 1
## 35 -0.07160415 2 0 0 -2 2
## 36 0.66838592 4 2 2 -2 2
## 37 0.41329976 2 1 2 0 1
## 38 0.66181269 4 2 3 -1 2
## 39 0.04139269 2 0 1 -1 2
## 40 0.86746749 5 4 2 -3 1
## 41 0.65417654 4 2 2 -2 2
## 42 0.56229286 2 1 2 0 1
## 43 0.11394335 2 0 1 -1 2
## 44 -0.10790540 2 0 0 -2 2
## 45 0.02938378 2 1 1 -1 1
## 46 -0.07160415 2 0 0 -2 2
## 47 0.55750720 4 2 2 -2 2
## 48 0.48144263 2 1 2 0 1
## 49 0.14301480 2 0 2 0 2
## 50 0.66745295 4 1 2 -2 3
## 51 -0.10182352 1 0 1 0 1
## 52 0.66558099 4 1 2 -2 3
## 53 0.20951501 2 1 1 -1 1
## 54 0.50514998 2 1 2 0 1
## 55 0.59988307 2 1 1 -1 1
## 56 1.00432137 7 0 7 0 7
## 57 0.93500315 5 3 2 -3 2
## 58 0.72345567 5 0 2 -3 5
## 59 0.10380372 4 0 0 -4 4
## 60 0.83632412 4 1 1 -3 3
## 61 -0.01863449 3 0 0 -3 3
## 62 0.85186960 5 0 2 -3 5
## 63 0.80345712 4 1 3 -1 3
## 64 0.58206336 2 1 2 0 1
## 65 0.31386722 3 1 1 -2 2
## 66 1.00000000 5 1 1 -4 4
## 67 0.10720997 4 0 0 -4 4
## 68 0.88309336 4 0 0 -4 4
## 69 0.95664858 5 1 1 -4 4
## 70 0.27875360 3 1 1 -2 2
## 71 0.78675142 4 1 2 -2 3
## 72 0.80071708 5 1 1 -4 4
## 73 0.34830486 3 1 1 -2 2
## 74 -0.01637371 3 0 0 -3 3
## 75 0.74272513 3 1 1 -2 2
## 76 0.26481782 2 1 1 -1 1
## 77 1.60422605 4 3 4 0 1
## 78 1.13987909 2 1 2 0 1
## 79 1.14612804 3 2 3 0 1
## 80 0.46239800 3 2 2 -1 1
## 81 0.23804610 2 0 1 -1 2
## 82 1.50785587 5 4 5 0 1
## 83 0.71349054 3 2 2 -1 1
## 84 0.57054294 3 2 3 0 1
## 85 0.84010609 3 2 3 0 1
## 86 0.68394713 2 1 2 0 1
## 87 1.11394335 4 3 4 0 1
## 88 1.52762990 4 3 4 0 1
## 89 0.38021124 2 1 2 0 1
## 90 0.50105926 4 0 1 -3 4
## 91 0.48144263 3 1 1 -2 2
## 92 1.23299611 4 2 3 -1 2
## 93 0.11727130 3 0 2 -1 3
## 94 1.27646180 3 2 2 -1 1
## 95 0.20139712 3 1 2 -1 2
## 96 1.11058971 4 2 2 -2 2
## 97 1.07188201 3 2 2 -1 1
## 98 1.14921911 3 2 2 -1 1
## 99 3.24054925 4 2 1 -3 2
## 100 2.83122969 4 2 2 -2 2
## 101 3.00000000 4 1 5 1 3
## 102 2.81224470 3 2 3 0 1
## 103 1.68574174 4 2 4 0 2
## 104 2.01703334 4 2 4 0 2
## 105 0.55022835 3 1 4 1 2
## 106 1.59659710 4 2 2 -2 2
## 107 0.95664858 2 1 2 0 1
## 108 1.63042788 3 1 2 -1 2
## 109 0.26951294 2 1 0 -2 1
## 110 2.56110138 2 1 2 0 1
## 111 1.20139712 2 1 2 0 1
## 112 4.51851394 4 1 4 0 3
## 113 1.20139712 2 0 0 -2 2
## 114 1.20139712 3 0 3 0 3
## 115 4.66838592 5 4 5 0 1
## 116 3.61172331 3 2 3 0 1
Permissive idiosyncrasy: When positive epistasis enables node visitation AND mean epistasis would have prevented it
adaptive_traj_access %>%
drop_na() %>%
filter(avg > log10(1.5) & access_w_idio > 0 & access_w_mean < 0 & means < 0)
## genotype avg pos mutations likely
## 1 xxx0xx 2.5755328 p1|p21|p26|p30|p94 5 FALSE
## 2 xx0xxx 3.1289862 p1|p21|p28|p30|p94 5 FALSE
## 3 xx000x 1.0635938 p1|p21|p94 3 FALSE
## 4 x0xxx0 0.2566377 p1|p26|p28|p30 4 FALSE
## 5 x0xxxx 0.5135761 p1|p26|p28|p30|p94 5 FALSE
## 6 x0x0xx 0.2071172 p1|p26|p30|p94 4 FALSE
## 7 00xx0x 0.7498001 p26|p28|p94 3 FALSE
## 8 xxx0x 0.2309903 p72|p193|p258|p273 4 TRUE
## 9 0xx0x00 0.2287509 p215|p219|p224 3 FALSE
## 10 x0x0xxx 0.2083878 p41|p219|p224|p225|p227 5 FALSE
## 11 xxxx0x0 0.3058285 p41|p215|p219|p222|p225 5 FALSE
## 12 xxx00xx 0.4055680 p41|p215|p219|p225|p227 5 FALSE
## 13 xx0000x 0.4412514 p41|p215|p227 3 FALSE
## 14 x0xxxx0 0.4244484 p41|p219|p222|p224|p225 5 FALSE
## 15 x0xx0xx 0.6719119 p41|p219|p222|p225|p227 5 FALSE
## 16 x00xxxx 0.3339212 p41|p222|p224|p225|p227 5 FALSE
## 17 x00x00x 0.4093652 p41|p222|p227 3 FALSE
## 18 x0000xx 0.3800391 p41|p225|p227 3 FALSE
## 19 00xx 0.2092675 p212|p213 2 FALSE
## 20 xxx0x0 0.1837956 p66|p76|p124|p156 4 FALSE
## 21 x00xx0 0.1772274 p67|p158|p180 3 FALSE
## 22 xxx0x0 0.8786927 p233|p254|p271|p306 4 TRUE
## 23 xxx00x 0.9425507 p233|p254|p271|p313 4 FALSE
## 24 0xxx0x 0.2459981 p254|p271|p272|p313 4 FALSE
## 25 00xx0 0.1815151 p104|p182 2 FALSE
## 26 0x0x0 1.2923763 p42|p182 2 FALSE
## unique_id enzyme_name measure_type cond means
## 1 DHFR_ic75_palmer DHFR ic75 palmer -1.019035014
## 2 DHFR_ic75_palmer DHFR ic75 palmer -0.465581551
## 3 DHFR_ic75_palmer DHFR ic75 palmer -0.373479384
## 4 DHFR_ic75_palmer DHFR ic75 palmer -1.177492205
## 5 DHFR_ic75_palmer DHFR ic75 palmer -3.080991709
## 6 DHFR_ic75_palmer DHFR ic75 palmer -0.045612248
## 7 DHFR_ic75_palmer DHFR ic75 palmer -0.043087603
## 8 MPH_catact_ZnPTM MPH catact ZnPTM -0.118379979
## 9 NfsA_ec50_2039 NfsA ec50 2039 -0.036461257
## 10 NfsA_ec50_2039 NfsA ec50 2039 -0.039287044
## 11 NfsA_ec50_3637 NfsA ec50 3637 -0.182582709
## 12 NfsA_ec50_3637 NfsA ec50 3637 -0.048249881
## 13 NfsA_ec50_3637 NfsA ec50 3637 -0.181880262
## 14 NfsA_ec50_3637 NfsA ec50 3637 -0.198513642
## 15 NfsA_ec50_3637 NfsA ec50 3637 -0.109238294
## 16 NfsA_ec50_3637 NfsA ec50 3637 -0.078881834
## 17 NfsA_ec50_3637 NfsA ec50 3637 -0.055554618
## 18 NfsA_ec50_3637 NfsA ec50 3637 -0.360855472
## 19 OXA-48_ic50_CAZtraj1 OXA-48 ic50 CAZtraj1 -0.001843610
## 20 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.002308696
## 21 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.010609253
## 22 PTE_catact_2NH PTE catact 2NH -0.333523949
## 23 PTE_catact_2NH PTE catact 2NH -0.226875448
## 24 PTE_catact_2NH PTE catact 2NH -0.230150706
## 25 TEM_MIC_weinreich TEM MIC weinreich -0.638009314
## 26 TEM_MIC_weinreich TEM MIC weinreich -0.253191980
## traj_effects access access_no_idio access_mean access_w_mean access_w_idio
## 1 2.3138672 5 0 0 -5 5
## 2 2.2787536 4 2 2 -2 2
## 3 1.9831751 3 1 0 -3 2
## 4 2.3222193 3 0 0 -3 3
## 5 2.3242825 4 1 0 -4 3
## 6 2.2174839 4 1 1 -3 3
## 7 2.0492180 2 1 1 -1 1
## 8 2.3117539 4 3 3 -1 1
## 9 -0.1313556 2 0 0 -2 2
## 10 0.8674675 5 4 2 -3 1
## 11 0.9350032 5 3 2 -3 2
## 12 0.8363241 4 1 1 -3 3
## 13 0.3138672 3 1 1 -2 2
## 14 1.0000000 5 1 1 -4 4
## 15 0.8830934 4 0 0 -4 4
## 16 0.8007171 5 1 1 -4 4
## 17 0.3483049 3 1 1 -2 2
## 18 0.7427251 3 1 1 -2 2
## 19 0.2648178 2 1 1 -1 1
## 20 0.7134905 3 2 2 -1 1
## 21 1.0718820 3 2 2 -1 1
## 22 3.2405492 4 2 1 -3 2
## 23 2.8312297 4 2 2 -2 2
## 24 1.5965971 4 2 2 -2 2
## 25 0.2695129 2 1 0 -2 1
## 26 1.2013971 2 0 0 -2 2
Even more interesting are the nodes with are either ENTIRELY accessible or inaccessible due to epistasis
adaptive_traj_access %>%
drop_na() %>%
filter(avg > log10(1.5) & access_no_idio == 0 & access > 0)
## genotype avg pos mutations likely
## 1 xxxx0x 10.4358638 p1|p21|p26|p28|p94 5 FALSE
## 2 xxx0xx 2.5755328 p1|p21|p26|p30|p94 5 FALSE
## 3 x0xxx0 0.2566377 p1|p26|p28|p30 4 FALSE
## 4 x00x0x 1.0706822 p1|p28|p94 3 FALSE
## 5 0xx00x 5.0316956 p21|p26|p94 3 FALSE
## 6 x0xxx 1.8737920 p21|p28|p30|p94 4 FALSE
## 7 x00xx 0.9335231 p72|p271|p273 3 FALSE
## 8 0xx0x00 0.2287509 p215|p219|p224 3 FALSE
## 9 0xx00x0 0.4841288 p215|p219|p225 3 FALSE
## 10 xxxxx0x 0.1907594 p41|p215|p219|p222|p224|p227 6 FALSE
## 11 xx00x00 0.2626518 p41|p215|p224 3 FALSE
## 12 x0x0x00 0.1951751 p41|p219|p224 3 FALSE
## 13 0xxx00x 0.3133913 p215|p219|p222|p227 4 FALSE
## 14 0xx0x0x 0.2739510 p215|p219|p224|p227 4 FALSE
## 15 0x0xx0x 0.2141675 p215|p222|p224|p227 4 FALSE
## 16 0x0000x 0.2929325 p215|p227 2 FALSE
## 17 00x0x00 0.2124097 p219|p224 2 FALSE
## 18 xxxxxxx 1.9130555 p41|p215|p219|p222|p224|p225|p227 7 TRUE
## 19 xxxx00x 0.7515982 p41|p215|p219|p222|p227 5 FALSE
## 20 xxx0x0x 0.5787185 p41|p215|p219|p224|p227 5 FALSE
## 21 xx0xx0x 0.4530458 p41|p215|p222|p224|p227 5 FALSE
## 22 xx0x0xx 0.7581143 p41|p215|p222|p225|p227 5 FALSE
## 23 x0xxx0x 0.7129843 p41|p219|p222|p224|p227 5 FALSE
## 24 x0xx0xx 0.6719119 p41|p219|p222|p225|p227 5 FALSE
## 25 x000x0x 0.5949846 p41|p224|p227 3 FALSE
## 26 x0x0xx 0.4265923 p66|p124|p156|p243 4 FALSE
## 27 0xxxxx 0.4960242 p142|p154|p158|p180|p215 5 FALSE
## 28 xxx0x0 0.2375477 p67|p142|p154|p180 4 FALSE
## 29 0x0x0 1.2923763 p42|p182 2 FALSE
## 30 xxxx0 1.4522693 p4205|p42|p104|p182 4 FALSE
## unique_id enzyme_name measure_type cond means
## 1 DHFR_ic75_palmer DHFR ic75 palmer 6.84129599
## 2 DHFR_ic75_palmer DHFR ic75 palmer -1.01903501
## 3 DHFR_ic75_palmer DHFR ic75 palmer -1.17749221
## 4 DHFR_ic75_palmer DHFR ic75 palmer 1.64784156
## 5 DHFR_ic75_palmer DHFR ic75 palmer 1.69529922
## 6 DHFR_ki_trajg DHFR ki trajg 1.67259092
## 7 MPH_catact_ZnPTM MPH catact ZnPTM 1.13823460
## 8 NfsA_ec50_2039 NfsA ec50 2039 -0.03646126
## 9 NfsA_ec50_2039 NfsA ec50 2039 0.35177779
## 10 NfsA_ec50_2039 NfsA ec50 2039 0.30651198
## 11 NfsA_ec50_2039 NfsA ec50 2039 0.08912188
## 12 NfsA_ec50_2039 NfsA ec50 2039 0.05163206
## 13 NfsA_ec50_3637 NfsA ec50 3637 0.16030008
## 14 NfsA_ec50_3637 NfsA ec50 3637 0.09261463
## 15 NfsA_ec50_3637 NfsA ec50 3637 0.01220592
## 16 NfsA_ec50_3637 NfsA ec50 3637 0.18769791
## 17 NfsA_ec50_3637 NfsA ec50 3637 0.07485416
## 18 NfsA_ec50_3637 NfsA ec50 3637 1.91305548
## 19 NfsA_ec50_3637 NfsA ec50 3637 0.20663486
## 20 NfsA_ec50_3637 NfsA ec50 3637 0.22653671
## 21 NfsA_ec50_3637 NfsA ec50 3637 0.27642964
## 22 NfsA_ec50_3637 NfsA ec50 3637 0.47986213
## 23 NfsA_ec50_3637 NfsA ec50 3637 0.03347017
## 24 NfsA_ec50_3637 NfsA ec50 3637 -0.10923829
## 25 NfsA_ec50_3637 NfsA ec50 3637 0.12064083
## 26 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 0.38623823
## 27 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.25971748
## 28 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 0.16493203
## 29 TEM_MIC_weinreich TEM MIC weinreich -0.25319198
## 30 TEM_MIC_weinreich TEM MIC weinreich 1.58700601
## traj_effects access access_no_idio access_mean access_w_mean access_w_idio
## 1 2.29446623 3 0 1 -2 3
## 2 2.31386722 5 0 0 -5 5
## 3 2.32221929 3 0 0 -3 3
## 4 2.28330123 3 0 3 0 3
## 5 1.36361198 3 0 1 -2 3
## 6 4.19865709 4 0 4 0 4
## 7 0.34830486 2 0 2 0 2
## 8 -0.13135556 2 0 0 -2 2
## 9 -0.08144547 2 0 1 -1 2
## 10 -0.14448084 2 0 4 2 2
## 11 -0.07160415 2 0 0 -2 2
## 12 0.04139269 2 0 1 -1 2
## 13 0.11394335 2 0 1 -1 2
## 14 -0.10790540 2 0 0 -2 2
## 15 -0.07160415 2 0 0 -2 2
## 16 0.14301480 2 0 2 0 2
## 17 -0.10182352 1 0 1 0 1
## 18 1.00432137 7 0 7 0 7
## 19 0.72345567 5 0 2 -3 5
## 20 0.10380372 4 0 0 -4 4
## 21 -0.01863449 3 0 0 -3 3
## 22 0.85186960 5 0 2 -3 5
## 23 0.10720997 4 0 0 -4 4
## 24 0.88309336 4 0 0 -4 4
## 25 -0.01637371 3 0 0 -3 3
## 26 0.23804610 2 0 1 -1 2
## 27 0.50105926 4 0 1 -3 4
## 28 0.11727130 3 0 2 -1 3
## 29 1.20139712 2 0 0 -2 2
## 30 1.20139712 3 0 3 0 3
adaptive_traj_access %>%
drop_na() %>%
filter(avg < log10(1/1.5) & access_no_idio > 0 & access == 0)
## genotype avg pos mutations likely
## 1 xx0x0x -5.2140863 p1|p21|p28|p94 4 FALSE
## 2 0xx000 -4.0673669 p21|p26 2 FALSE
## 3 0xxx0x -4.7257532 p21|p26|p28|p94 4 FALSE
## 4 0x0xx0 -1.6714200 p21|p28|p30 3 FALSE
## 5 0x00xx -1.5696224 p21|p30|p94 3 FALSE
## 6 000xxx -2.0436471 p28|p30|p94 3 FALSE
## 7 0x000x0 -0.8913091 p215|p225 2 FALSE
## 8 x000x00 -0.1999418 p41|p224 2 FALSE
## 9 0x000x0 -0.7363049 p215|p225 2 FALSE
## 10 x00xx00 -0.3562048 p41|p222|p224 3 FALSE
## 11 x00000x -0.4283630 p41|p227 2 FALSE
## 12 x0x00x -0.4946517 p66|p124|p243 3 FALSE
## 13 xxx000 -0.2525100 p67|p142|p154 3 FALSE
## 14 x00xxx -0.9373094 p233|p272|p306|p313 4 FALSE
## unique_id enzyme_name measure_type cond means
## 1 DHFR_ic75_palmer DHFR ic75 palmer -0.22894521
## 2 DHFR_ic75_palmer DHFR ic75 palmer 0.53515465
## 3 DHFR_ic75_palmer DHFR ic75 palmer -0.82781359
## 4 DHFR_ic75_palmer DHFR ic75 palmer 0.76025803
## 5 DHFR_ic75_palmer DHFR ic75 palmer 0.27945161
## 6 DHFR_ic75_palmer DHFR ic75 palmer 0.68005918
## 7 NfsA_ec50_2039 NfsA ec50 2039 -0.24105803
## 8 NfsA_ec50_2039 NfsA ec50 2039 0.05849991
## 9 NfsA_ec50_3637 NfsA ec50 3637 -0.17123523
## 10 NfsA_ec50_3637 NfsA ec50 3637 -0.11793908
## 11 NfsA_ec50_3637 NfsA ec50 3637 -0.05626247
## 12 OXA-48_ic50_CAZtraj2 OXA-48 ic50 CAZtraj2 -0.13701889
## 13 OXA-48_ic50_CAZtraj3 OXA-48 ic50 CAZtraj3 -0.10303183
## 14 PTE_catact_2NH PTE catact 2NH -0.27806987
## traj_effects access access_no_idio access_mean access_w_mean access_w_idio
## 1 -3.031517051 0 3 1 1 -3
## 2 -3.031517051 0 2 2 2 -2
## 3 -0.422508200 0 4 4 4 -4
## 4 -0.804100348 0 1 2 2 -1
## 5 -0.543633967 0 2 2 2 -2
## 6 -0.157390760 0 2 3 3 -2
## 7 -0.501689446 0 1 1 1 -1
## 8 -0.151810883 0 1 2 2 -1
## 9 -0.327902142 0 1 1 1 -1
## 10 -0.287350298 0 2 2 2 -2
## 11 -0.449771647 0 1 1 1 -1
## 12 0.100370545 0 2 2 2 -2
## 13 -0.008773924 0 3 2 2 -3
## 14 0.474216264 0 3 1 1 -3
Specific epistasis examples, starting with PTE
with_epi = fit_land %>%
filter(unique_id == "PTE_catact_2NH" & id == "111010") %>%
pull(effect)
epi = higher_df %>% filter(partial_id == "PTE_catact_2NH" & genotype == "xxx0x0") %>% pull(avg)
mean_epi = adaptive_traj %>% filter(unique_id == "PTE_catact_2NH" & genotype == "xxx0x0") %>% pull(means)
wo_epi = with_epi - epi
w_mean_epi = with_epi - epi + mean_epi
fig_4_new = fit_land %>%
filter(unique_id == "PTE_catact_2NH") %>%
filter(id == "000010" | id == "010010" | id == "110010" | id == "111010" | id == "111110" | id == "111111") %>%
add_row(id = "000000",
effect = 0,
muts = 0,
junk1 = NA,
junk2 = NA,
enz = "PTE",
unique_id = "PTE_catact_2NH",
.before = 1) %>%
add_row(id = "alt",
effect = wo_epi,
muts = 4,
junk1 = NA,
junk2 = NA,
enz = "PTE",
unique_id = "PTE_catact_2NH",
.before = 1) %>%
add_row(id = "alt2",
effect = w_mean_epi,
muts = 4,
junk1 = NA,
junk2 = NA,
enz = "PTE",
unique_id = "PTE_catact_2NH",
.before = 1) %>%
select(-c("junk1", "junk2")) %>%
mutate(muts = factor(muts)) %>%
ggplot(aes(x = muts, y = effect)) +
geom_point(shape = 1, size = 3) +
labs(x = "Step",
y = expression("log"[10]*" Function") ) +
theme_classic() +
theme(axis.line = element_line(size = 0.2, color = "black"),
axis.ticks = element_line(size = 0.2, color = "black"),
text = element_text(size = 9),
axis.text = element_text(size = 8, color = "black"))
fig_4_new
#ggsave("fig_4A_new.svg", fig_4_new, width = 180/2.5, height = 247/4, dpi = 300, units = "mm")
Next one is MPH
with_epi = fit_land %>%
filter(unique_id == "MPH_catact_ZnPTM" & id == "10001") %>%
pull(effect)
epi = higher_df %>% filter(partial_id == "MPH_catact_ZnPTM" & genotype == "x000x") %>% pull(avg)
mean_epi = adaptive_traj %>% filter(unique_id == "MPH_catact_ZnPTM" & genotype == "x000x") %>% pull(means)
wo_epi = with_epi - epi
w_mean_epi = with_epi - epi + mean_epi
fig_4b_new = fit_land %>%
filter(unique_id == "MPH_catact_ZnPTM") %>%
filter(id == "10000" | id == "10001" | id == "11001" | id == "11101" | id == "11111") %>%
add_row(id = "00000",
effect = 0,
muts = 0,
junk1 = NA,
junk2 = NA,
enz = "MPH",
unique_id = "MPH_catact_ZnPTM",
.before = 1) %>%
add_row(id = "alt",
effect = wo_epi,
muts = 2,
junk1 = NA,
junk2 = NA,
enz = "MPH",
unique_id = "MPH_catact_ZnPTM") %>%
add_row(id = "alt2",
effect = w_mean_epi,
muts = 2,
junk1 = NA,
junk2 = NA,
enz = "MPH",
unique_id = "MPH_catact_ZnPTM") %>%
select(-c("junk1", "junk2")) %>%
mutate(muts = factor(muts)) %>%
ggplot(aes(x = muts, y = effect)) +
geom_point(shape = 1, size = 3) +
labs(x = "Step",
y = expression("log"[10]*" Function") ) +
theme_classic() +
theme(axis.line = element_line(size = 0.2, color = "black"),
axis.ticks = element_line(size = 0.2, color = "black"),
text = element_text(size = 9),
axis.text = element_text(size = 8, color = "black"))
fig_4b_new
#ggsave("fig_4B_new.svg", fig_4b_new, width = 180/2.5, height = 247/4, dpi = 300, units = "mm")
Here we attempt to see whether the function of the genotype F is an indicator of the magnitude of epistasis
higher_df_start_to_epi = higher_df %>%
unite("partial_id", c(Enzyme, Measurement, Condition)) %>%
mutate(from = str_replace_all(genotype, "x", "0"),
to = str_replace_all(genotype, "x", "1"))
start_effect = c()
for(i in 1:dim(higher_df_start_to_epi)[1]) {
if(!str_detect(higher_df_start_to_epi$from[i], "1")) {
start_effect = c(start_effect, 0) # If starts from WT, start effect is 0
} else {
start_effect = c(start_effect, filter(fit_land, unique_id == higher_df_start_to_epi$partial_id[i] & id == higher_df_start_to_epi$from[i])
%>% pull(effect))
}
}
higher_df_start_to_epi$start_effect = start_effect
The higher_df_start_to_epi tibble contains information regarding specific epistasis in the avg variable and starting effect of the genotype in the start_effect. We can plot the correlation for all landscapes
higher_df_start_to_epi %>%
filter(mutations > 1) %>%
ggplot(aes(x = start_effect, y = avg)) +
geom_point() +
theme_classic()
Then again for adaptive landscapes only
higher_df_start_to_epi %>%
filter(mutations > 1 & partial_id %in% adaptive_trajectories) %>%
ggplot(aes(x = start_effect, y = avg)) +
geom_point() +
theme_classic()
What if we facet adaptive landscapes by order?
higher_df_start_to_epi %>%
filter(mutations > 1 & partial_id %in% adaptive_trajectories) %>%
ggplot(aes(x = start_effect, y = avg)) +
geom_point() +
facet_wrap(~ mutations) +
theme_classic()
Due to the differences in strength of functional affects across landscapes, maybe its important to separate by landscape and see whether there is a difference between the correlation of mutation effects and starting function VS the correlation of epistasis and starting function
trajectory_names = c(
DHFR_ic75_palmer = "DHFR Palmer et al.",
DHFR_ki_trajg = "DHFR Traj. G",
DHFR_ki_trajr = "DHFR Traj. R",
MPH_catact_ZnPTM = "MPH Traj. Zn",
NfsA_ec50_2039 = "NfsA Traj. 20_39",
NfsA_ec50_3637 = "NfsA Traj. 36_37",
`OXA-48_ic50_CAZtraj1` = "OXA-48 CAZ Traj. 1",
`OXA-48_ic50_CAZtraj2` = "OXA-48 CAZ Traj. 2",
`OXA-48_ic50_CAZtraj3` = "OXA-48 CAZ Traj. 3",
PTE_catact_2NH = "PTE Traj. 2NH",
TEM_MIC_weinreich = "TEM Weinreich et al.")
global_labeller = labeller(
partial_id = trajectory_names
)
dimish_del_f = higher_df_start_to_epi %>%
filter(mutations == 1 & partial_id %in% adaptive_trajectories) %>%
ggplot(aes(x = start_effect, y = avg)) +
stat_cor(aes(label = paste(..r.label.., sep = "~~")), method = "spearman", cor.coef.name = "rho", label.x.npc = "middle",
label.y.npc = "top", r.accuracy = 0.01, size = 2.5, color = "black") +
geom_point() +
geom_smooth(method = 'loess', formula = y ~ x) +
facet_wrap(~ partial_id,
scales = "free",
labeller = global_labeller) +
theme_classic() +
labs(x = expression("Starting Function ("*italic("F")*")"),
y = expression(Delta*italic("F"))) +
theme(text = element_text(size=9), axis.text = element_text(size=8, color = "black"),
legend.position = "none", axis.line=element_line()) #+
# scale_y_continuous(limits=c(-5,5)) +
#scale_x_continuous(limits=c(-3,5))
dimish_epi = higher_df_start_to_epi %>%
filter(mutations > 1 & partial_id %in% adaptive_trajectories) %>%
ggplot(aes(x = start_effect, y = avg)) +
stat_cor(aes(label = paste(..r.label.., sep = "~~")), method = "spearman", cor.coef.name = "rho", label.x.npc = "middle",
label.y.npc = "top", r.accuracy = 0.01, size = 2.5, color = "black") +
geom_point() +
geom_smooth(method = 'loess', formula = y ~ x) +
facet_wrap(~ partial_id,
scales = "free",
labeller = global_labeller) +
theme_classic() +
labs(x = expression("Starting Function ("*italic("F")*")"),
y = expression(epsilon)) +
theme(text = element_text(size=9), axis.text = element_text(size=8, color = "black"),
legend.position = "none", axis.line=element_line())
dimish_del_f
dimish_epi
#ggsave("supp_fig_3A.svg", dimish_del_f, width = 180, height = 247/2, dpi = 300, units = "mm")
#ggsave("supp_fig_3B.svg", dimish_epi, width = 180, height = 247/2, dpi = 300, units = "mm")